Estimate aggregate output rows using existing NDV statistics (#20926)

## Which issue does this PR close?
Part of #20766

## Rationale for this change
Grouped aggregations currently estimate output rows as input_rows,
ignoring available NDV statistics. Spark's AggregateEstimation and
Trino's AggregationStatsRule both use NDV products to tighten this
estimate. This PR is highly referenced by both.


- [Spark
reference](https://github.com/apache/spark/blob/e8d8e6a8d040d26aae9571e968e0c64bda0875dc/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/AggregateEstimation.scala#L38-L61)
- [Trino
reference](https://github.com/trinodb/trino/blob/43c8c3ba8bff814697c5926149ce13b9532f030b/core/trino-main/src/main/java/io/trino/cost/AggregationStatsRule.java#L92-L101)

## What changes are included in this PR?
- Estimate aggregate output rows as min(input_rows, product(NDV_i +
null_adj_i) * grouping_sets)
- Cap by Top K limit when active since output row cannot be higher than
K
- Propagate distinct_count from child stats to group-by output columns

## Are these changes tested?
Yes existing and new tests that cover different scenarios and edge cases


## Are there any user-facing changes?
No

Author: buraksenn

datafusion/core/tests/physical_optimizer/partition_statistics.rs

@@ -935,7 +935,10 @@ mod test {
             num_rows: Precision::Exact(0),
             total_byte_size: Precision::Absent,
             column_statistics: vec![
-                ColumnStatistics::new_unknown(),
+                ColumnStatistics {
+                    distinct_count: Precision::Exact(0),
+                    ..ColumnStatistics::new_unknown()
+                },
                 ColumnStatistics::new_unknown(),
                 ColumnStatistics::new_unknown(),
             ],