Rationale for this change

Parquet files often contain columns with highly repetitive values (e.g., status codes, categories, constant metadata fields). Currently, Arrow reads these into dense arrays, materializing every value and consuming significant memory.

This PR implements direct reads of Parquet RLE (Run Length Encoded) data into Arrow REE (Run End Encoded) representation as described in #32339, using the existing read_dictionary API as inspiration for interface level changes. Like read_dictionary, this feature is currently only supported for columns with a Parquet physical type of BYTE_ARRAY, such as string or binary types.

Example usage:

 import pyarrow.parquet as pq
 # These columns will be directly read as Arrow run-end-encoded without full materialization if their Parquet representation was run-length-encoded.
 table = pq.read_table('data.parquet', read_ree=['category', 'status']) 

This is a considerably hefty feature so please let me know if there's anything I can do to help the review process (e.g. by splitting this into multiple smaller PRs). The way I implemented this is by adding a GetNextValueAndNumRepeats API to the RleBitPackedDecoder which skips materialization of all values for RleRuns while still going through BitPackedRuns bit by bit. I'm definitely open to suggestions on other approaches here.

Regarding performance, I am anecdotally observing an order of magnitude (i.e. ~10x) speedup for reading columns which have lots of repeated values and a slight performance degredation for columns which contain purely unique values when read with this feature enabled (noting that the feature is toggled by the user, so presumably they would have a good understanding of the shape of their data to decide whether to enable/disable this feature). I haven't done any scientific benchmarking outside of this; let me know if that would be helpful.

What changes are included in this PR?

1. Add ArrowReaderProperties::set_read_ree() / read_ree() methods to enable REE reading per-column
2. Implement GetNextValueAndNumRepeats() and GetNextValueAndNumRepeatsSpaced() methods inRleBitPackedDecoder
3. Add ByteArrayReeRecordReader for decoding Parquet BYTE_ARRAY columns to RunEndEncodedArray
4. Support REE decoding for both Plain and RLE_DICTIONARY encodings
5. Add Python bindings via read_ree_columns parameter in ParquetDataset and ParquetFile

Are these changes tested?

Yes, through included C++ unit tests and pytests.

Are there any user-facing changes?

Yes.

• GitHub Issue: [C++] Reading and Writing RLE data from/to Parquet #32339