Fallback for upsert when arrow cannot compare source rows with target rows (apache#1878)

<!-- Fixes apache#1711 -->

Upsert operations in PyIceberg rely on Arrow joins between source and
target rows. However, Arrow Acero cannot compare certain complex types —
like `struct`, `list`, and `map` — unless they’re part of the join key.
When such types exist in non-join columns, the upsert fails with an
error like:

```ArrowInvalid: Data type struct<...> is not supported in join non-key field venue_geo```

This PR introduces a **fallback mechanism**: if Arrow fails to join due to unsupported types, we fall back to comparing only the key columns. Non-key complex fields are ignored in the join condition, but still retained in the final upserted data.

---

```python
txn.upsert(df, join_cols=["match_id"])
```

> ❌ ArrowInvalid: Data type struct<...> is not supported in join non-key field venue_geo

---

```python
txn.upsert(df, join_cols=["match_id"])
```

> ✅ Successfully inserts or updates the record, skipping complex field comparison during join

---

Yes:
- A test was added to reproduce the failure scenario with complex non-key fields.
- The new behavior is verified by asserting that the upsert completes successfully using the fallback logic.

---

> ℹ️ **Note**
> This change does not affect users who do not include complex types in their schemas. For those who do, it improves resilience while preserving data correctness.

---

Yes — upserts involving complex non-key columns (like `struct`, `list`, or `map`) no longer fail. They now succeed by skipping unsupported comparisons during the join phase.

Author: koenvo

tests/table/test_upsert.py

@@ -674,7 +674,7 @@ def test_upsert_with_nulls(catalog: Catalog) -> None:
 
     schema = pa.schema(
         [
-            ("foo", pa.string()),
+            ("foo", pa.large_string()),
             ("bar", pa.int32()),
             ("baz", pa.bool_()),
         ]
@@ -702,7 +702,7 @@ def test_upsert_with_nulls(catalog: Catalog) -> None:
     upd = table.upsert(data_without_null, join_cols=["foo"])
     assert upd.rows_updated == 1
     assert upd.rows_inserted == 0
-    assert table.scan().to_arrow() == pa.Table.from_pylist(
+    assert table.scan().to_arrow().combine_chunks() == pa.Table.from_pylist(
         [
             {"foo": "apple", "bar": 7, "baz": False},
             {"foo": "banana", "bar": None, "baz": False},