[FEA] Add option to use experimental parquet reader in cudf-polars

**Is your feature request related to a problem? Please describe.**
As of 25.12, cudf-polars uses the chunked parquet reader in libcudf for parquet TableScan.

#17896 tracks the development of a new parquet reader API that includes:
* fine-grained control of the reader steps, rather than completing all IO and compute behind one API
* stateless APIs to provide easier retry options
* new filtering options with page stats and dictionary page inspection
* two-stage materialization of filter columns and payload columns

There is a new example of the reader in action over in #19469

**Describe the solution you'd like**
cudf-polars could add an optional TableScan implementation that uses the new APIs. Over time, cudf-polars could take advantage of new features in the reader.

| Project | Scope | Status |
|---|---|---|
| Add pylibcudf bindings for `cudf::io::experimental::hybrid_scan_reader` | May need some development for pylibcudf to handle all of the API patterns in [hybrid_scan.cpp](https://github.com/rapidsai/cudf/blob/branch-25.10/cpp/include/cudf/io/experimental/hybrid_scan.hpp) | (needs triage) |
| Add basic cudf-polars implementation that uses `hybrid_scan` APIs | Could still match the inputs and outputs but use `materialize_filter_columns_chunk` and `materialize_payload_columns_chunk` instead of `read_chunk`. cudf-polars would need to use KvikIO python API to prepare compressed parquet buffers for the new reader. | |
| Add switch to toggle between the two readers | Ideally this reader selection could be made available via PDSH CLI parameter, or environment variable could also work. | |
| Add option to use two-stage or single-stage materialization | materialize filter columns first, and then compute how many payload row groups and pages are needed? or materialize all of the row groups in one pass | |
| Add option to use column index for page pruning | Initial testing shows it's expensive to compute per-page predicates, but it may be worthwhile depending on query selectivity, page alignment and size, and how much work we do to speed up per-page predicate computation. | |

**Additional context**
There is also integration work to test the new reader APIs in Velox-cuDF and Spark-RAPIDS.