perf: Cast entire Date32 array to Date64 on 1st failure (apache#21948)

## Which issue does this PR close?

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- Closes apache#17152

## Rationale for this change

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in the issue then this section is not needed.
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your changes and offer better suggestions for fixes.
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- [x] 1. `Vec<Option<String>>` extra allocations
- Resolved in apache#20635 by using
`StringBuilder`

- [ ] 2. Per-row cast on fallback
  - Will be resolved in this PR
```rust
// When `Date32` formatting fails (due to time specifiers),
// the code called cast individually for each row instead of converting the entire array
let date64_value = cast(&data_array.slice(idx, 1), &Date64)?;
```

## What changes are included in this PR?

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There is no need to duplicate the description in the issue here but it
is sometimes worth providing a summary of the individual changes in this
PR.
-->

Cast the entire Date32 array to Date64 once on first failure, instead of
per-row
```rust
cast(data_array.as_ref(), &Date64
```

## Are these changes tested?

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1. Prevent the code from being accidentally broken by subsequent changes
4. Serve as another way to document the expected behavior of the code

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are they covered by existing tests)?
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Yes

- Add 2 benchmarks:
  - `Date32 + datetime` patterns (all rows trigger the fallback)
  - `Date32 + mixed patterns` (roughly half do)

```bash
  ┌─────────────────────────────────────┬───────────────────┬───────────┐
  │              Benchmark              │      Format       │  Change   │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ array_date_only_patterns_1000       │ array, date-only  │ No change │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ array_datetime_patterns_1000        │ array, datetime   │ -3.21%    │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ array_mixed_patterns_1000           │ array, mixed      │ +1.41%    │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ scalar_date_only_pattern_1000       │ scalar, date-only │ No change │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ scalar_datetime_pattern_1000        │ scalar, datetime  │ No change │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ array_date32_datetime_patterns_1000 │ array, datetime   │ -20.24%   │
  ├─────────────────────────────────────┼───────────────────┼───────────┤
  │ array_date32_mixed_patterns_1000    │ array, mixed      │ -15.74%   │
  └─────────────────────────────────────┴───────────────────┴───────────┘
```

<details>
<summary>Detail</summary>

```bash
cargo bench --bench to_char          
   Compiling datafusion-functions v53.1.0 (/Users/qmac/Documents/GitHub/datafusion/datafusion/functions)
    Finished `bench` profile [optimized] target(s) in 1m 10s
     Running benches/to_char.rs (target/release/deps/to_char-d2acea4b7a3e2fba)
Gnuplot not found, using plotters backend
Benchmarking to_char_array_date_only_patterns_1000: Warming up for 3.00Benchmarking to_char_array_date_only_patterns_1000: Collecting 100 sampto_char_array_date_only_patterns_1000
                        time:   [104.01 µs 104.37 µs 104.74 µs]
                        change: [−0.5692% +0.3244% +1.0788%] (p = 0.47 > 0.05)
                        No change in performance detected.
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) low mild
  2 (2.00%) high mild

Benchmarking to_char_array_datetime_patterns_1000: Warming up for 3.000Benchmarking to_char_array_datetime_patterns_1000: Collecting 100 samplto_char_array_datetime_patterns_1000
                        time:   [165.96 µs 166.43 µs 166.94 µs]
                        change: [−4.1660% −3.2055% −2.3626%] (p = 0.00 < 0.05)
                        Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
  2 (2.00%) low mild
  3 (3.00%) high mild
  1 (1.00%) high severe

Benchmarking to_char_array_mixed_patterns_1000: Collecting 100 samples to_char_array_mixed_patterns_1000
                        time:   [139.58 µs 140.02 µs 140.49 µs]
                        change: [+1.0116% +1.4122% +1.8178%] (p = 0.00 < 0.05)
                        Performance has regressed.
Found 3 outliers among 100 measurements (3.00%)
  1 (1.00%) low mild
  2 (2.00%) high mild

Benchmarking to_char_scalar_date_only_pattern_1000: Warming up for 3.00Benchmarking to_char_scalar_date_only_pattern_1000: Collecting 100 sampto_char_scalar_date_only_pattern_1000
                        time:   [60.073 µs 63.184 µs 66.309 µs]
                        change: [−4.0228% +0.6839% +6.1581%] (p = 0.79 > 0.05)
                        No change in performance detected.

Benchmarking to_char_scalar_datetime_pattern_1000: Warming up for 3.000Benchmarking to_char_scalar_datetime_pattern_1000: Collecting 100 samplto_char_scalar_datetime_pattern_1000
                        time:   [117.65 µs 124.76 µs 131.84 µs]
                        change: [−3.3009% +2.6889% +8.6148%] (p = 0.37 > 0.05)
                        No change in performance detected.
Found 30 outliers among 100 measurements (30.00%)
  16 (16.00%) low mild
  14 (14.00%) high mild

Benchmarking to_char_array_date32_datetime_patterns_1000: Warming up foBenchmarking to_char_array_date32_datetime_patterns_1000: Collecting 10to_char_array_date32_datetime_patterns_1000
                        time:   [289.67 µs 290.77 µs 291.88 µs]
                        change: [−20.661% −20.240% −19.863%] (p = 0.00 < 0.05)
                        Performance has improved.

Benchmarking to_char_array_date32_mixed_patterns_1000: Warming up for 3Benchmarking to_char_array_date32_mixed_patterns_1000: Collecting 100 sto_char_array_date32_mixed_patterns_1000
                        time:   [194.47 µs 195.28 µs 196.11 µs]
                        change: [−16.230% −15.738% −15.285%] (p = 0.00 < 0.05)
                        Performance has improved.
```

</details>


## Are there any user-facing changes?

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updated before approving the PR.
-->

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change` label.
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No

---------

Co-authored-by: Kumar Ujjawal <ujjawalpathak6@gmail.com>

Author: huymq1710

datafusion/functions/src/datetime/to_char.rs

@@ -248,6 +248,11 @@ fn to_char_array(args: &[ColumnarValue]) -> Result<ColumnarValue> {
         StringBuilder::with_capacity(data_array.len(), data_array.len() * fmt_len);
     let mut buffer = String::with_capacity(fmt_len);
 
+    // Lazily computed Date64 cast of the entire array, used when a Date32
+    // format string contains time specifiers that the Date32 formatter
+    // cannot handle. Cast once and reuse for all subsequent rows
+    let mut date64_array: Option<ArrayRef> = None;
+
     for idx in 0..data_array.len() {
         if format_array.is_null(idx) || data_array.is_null(idx) {
             builder.append_null();
@@ -266,13 +271,18 @@ fn to_char_array(args: &[ColumnarValue]) -> Result<ColumnarValue> {
         // buffer and `append_value` on success.
         match formatter.value(idx).write(&mut buffer) {
             Ok(()) => builder.append_value(&buffer),
-            // Retry with Date64 (see comment in to_char_scalar).
             Err(_) if data_type == &Date32 => {
                 buffer.clear();
-                let date64_value = cast(&data_array.slice(idx, 1), &Date64)?;
-                let retry_fmt =
-                    ArrayFormatter::try_new(date64_value.as_ref(), &format_options)?;
-                retry_fmt.value(0).write(&mut buffer)?;
+                let date64_ref = match &date64_array {
+                    Some(arr) => arr.as_ref(),
+                    None => {
+                        date64_array = Some(cast(data_array.as_ref(), &Date64)?);
+                        date64_array.as_ref().unwrap().as_ref()
+                    }
+                };
+                let retry_options = build_format_options(&Date64, format)?;
+                let retry_fmt = ArrayFormatter::try_new(date64_ref, &retry_options)?;
+                retry_fmt.value(idx).write(&mut buffer)?;
                 builder.append_value(&buffer);
             }
             Err(e) => return Err(e.into()),
@@ -307,11 +317,21 @@ mod tests {
 
     #[test]
     fn test_array_array() {
-        let array_array_data = vec![(
-            Arc::new(Date32Array::from(vec![18506, 18507])) as ArrayRef,
-            StringArray::from(vec!["%Y::%m::%d", "%Y::%m::%d %S::%M::%H %f"]),
-            StringArray::from(vec!["2020::09::01", "2020::09::02 00::00::00 000000000"]),
-        )];
+        let array_array_data = vec![
+            (
+                Arc::new(Date32Array::from(vec![18506, 18507])) as ArrayRef,
+                StringArray::from(vec!["%Y::%m::%d", "%Y::%m::%d %S::%M::%H %f"]),
+                StringArray::from(vec![
+                    "2020::09::01",
+                    "2020::09::02 00::00::00 000000000",
+                ]),
+            ),
+            (
+                Arc::new(Date32Array::from(vec![18506, 18507])) as ArrayRef,
+                StringArray::from(vec!["%Y::%m::%d %H:%M:%S", "%d-%m-%Y %H:%M"]),
+                StringArray::from(vec!["2020::09::01 00:00:00", "02-09-2020 00:00"]),
+            ),
+        ];
 
         for (value, format, expected) in array_array_data {
             let batch_len = value.len();