## What changes were proposed in this pull request?

This PR targets to explicitly specify supported types in Pandas UDFs.
The main change here is to add a deduplicated and explicit type checking in `returnType` ahead with documenting this; however, it happened to fix multiple things.

1. Currently, we don't support `BinaryType` in Pandas UDFs, for example, see:

    ```python
    from pyspark.sql.functions import pandas_udf
    pudf = pandas_udf(lambda x: x, "binary")
    df = spark.createDataFrame([[bytearray(1)]])
    df.select(pudf("_1")).show()
    ```
    ```
    ...
    TypeError: Unsupported type in conversion to Arrow: BinaryType
    ```

    We can document this behaviour for its guide.

2. Also, the grouped aggregate Pandas UDF fails fast on `ArrayType` but seems we can support this case.

    ```python
    from pyspark.sql.functions import pandas_udf, PandasUDFType
    foo = pandas_udf(lambda v: v.mean(), 'array<double>', PandasUDFType.GROUPED_AGG)
    df = spark.range(100).selectExpr("id", "array(id) as value")
    df.groupBy("id").agg(foo("value")).show()
    ```

    ```
    ...
     NotImplementedError: ArrayType, StructType and MapType are not supported with PandasUDFType.GROUPED_AGG
    ```

3. Since we can check the return type ahead, we can fail fast before actual execution.

    ```python
    # we can fail fast at this stage because we know the schema ahead
    pandas_udf(lambda x: x, BinaryType())
    ```

## How was this patch tested?

Manually tested and unit tests for `BinaryType` and `ArrayType(...)` were added.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #20531 from HyukjinKwon/pudf-cleanup.