[SPARK-27463][PYTHON] Support Dataframe Cogroup via Pandas UDFs
### What changes were proposed in this pull request?
Adds a new cogroup Pandas UDF. This allows two grouped dataframes to be cogrouped together and apply a (pandas.DataFrame, pandas.DataFrame) -> pandas.DataFrame UDF to each cogroup.
**Example usage**
```
from pyspark.sql.functions import pandas_udf, PandasUDFType
df1 = spark.createDataFrame(
[(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
("time", "id", "v1"))
df2 = spark.createDataFrame(
[(20000101, 1, "x"), (20000101, 2, "y")],
("time", "id", "v2"))
pandas_udf("time int, id int, v1 double, v2 string", PandasUDFType.COGROUPED_MAP)
def asof_join(l, r):
return pd.merge_asof(l, r, on="time", by="id")
df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show()
```
+--------+---+---+---+
| time| id| v1| v2|
+--------+---+---+---+
|20000101| 1|1.0| x|
|20000102| 1|3.0| x|
|20000101| 2|2.0| y|
|20000102| 2|4.0| y|
+--------+---+---+---+
### How was this patch tested?
Added unit test test_pandas_udf_cogrouped_map
Closes #24981 from d80tb7/SPARK-27463-poc-arrow-stream.
Authored-by: Chris Martin <chris@cmartinit.co.uk>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
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