[SPARK-54531][PYTHON] Introduce ArrowStreamAggPandasUDFSerializer

[Yicong-Huang]

What changes were proposed in this pull request?

This PR separates SQL_GROUPED_AGG_PANDAS_UDF and SQL_WINDOW_AGG_PANDAS_UDF into a dedicated serializer ArrowStreamAggPandasUDFSerializer, aligning with the existing ArrowStreamAggArrowUDFSerializer architecture.

Why are the changes needed?

1. Input/Output type differences: Aggregation UDFs (SQL_GROUPED_AGG_PANDAS_UDF and SQL_WINDOW_AGG_PANDAS_UDF) have different input/output types compared to grouped map UDFs:

   • Aggregation UDFs: Input is pd.Series (entire group/partition), output is scalar
   • Grouped map UDFs: Input is (keys, vals) where vals is pd.DataFrame, output is pd.DataFrame

2. Multi-UDF support: Aggregation UDFs support multiple UDFs in a single projection/aggregation, while grouped map UDFs do not.

Does this PR introduce any user-facing change?

No. This is an internal refactoring that does not change the public API or behavior. The serialization logic remains functionally equivalent.

How was this patch tested?

All existing tests continue to pass, and a new multi-UDF test (test_pandas_udf_window.py::WindowPandasUDFTests::test_multiple_udfs) was added.

Was this patch authored or co-authored using generative AI tooling?

No

[zhengruifeng]

remove [CORE] and [SQL] from the title, since it's not related to spark core or sql

[Yicong-Huang]

remove [CORE] and [SQL] from the title, since it's not related to spark core or sql

I added them according to the labels added by github actions. Are they accurate?

[zhengruifeng]

remove [CORE] and [SQL] from the title, since it's not related to spark core or sql

I added them according to the labels added by github actions. Are they accurate?

they are not very accurate

[zhengruifeng]

merged to master