### What changes were proposed in this pull request?
This PR proposes to implement `TimestampNTZType` support in PySpark's `SparkSession.createDataFrame`, `DataFrame.toPandas`, Python UDFs, and pandas UDFs with and without Arrow.
### Why are the changes needed?
To complete `TimestampNTZType` support.
### Does this PR introduce _any_ user-facing change?
Yes.
- Users now can use `TimestampNTZType` type in `SparkSession.createDataFrame`, `DataFrame.toPandas`, Python UDFs, and pandas UDFs with and without Arrow.
- If `spark.sql.timestampType` is configured to `TIMESTAMP_NTZ`, PySpark will infer the `datetime` without timezone as `TimestampNTZType`. If it has a timezone, it will be inferred as `TimestampType` in `SparkSession.createDataFrame`.
- If `TimestampType` and `TimestampNTZType` conflict during merging inferred schema, `TimestampType` has a higher precedence.
- If the type is `TimestampNTZType`, treat this internally as an unknown timezone, and compute w/ UTC (same as JVM side), and avoid localization externally.
### How was this patch tested?
Manually tested and unittests were added.
Closes#33876 from HyukjinKwon/SPARK-36626.
Lead-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Co-authored-by: Dominik Gehl <dog@open.ch>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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