fix: Fix hive partition pruning not filtering out __HIVE_DEFAULT_PARTITION__

[nameexhaustion]

• Fixes Polars 1.30.0 does not filter out nulls in hive-partitioned datasets #23005

We currently evaluate hive predicates using a skip_batch_predicate that was designed for parquet statistics. This is essentially an expression that operates on a DataFrame containing {column}_min, {column}_max columns.

The hive partitions were converted to such a DataFrame, however this is not compatible as skip_batch_predicate interprets NULL as "statistics are missing" and indicates that the data must be read, whereas in hive a NULL indicates that the actual row values are all NULL.

This PR changes it to directly use the original predicate expression. It is more of a quick fix as I am expecting to do larger refactors around predicate handling in the future for iceberg.

[codecov]

Codecov Report

Attention: Patch coverage is 80.72289% with 16 lines in your changes missing coverage. Please review.

Project coverage is 80.22%. Comparing base (7768a67) to head (4007be8).
Report is 8 commits behind head on main.

Files with missing lines	Patch %	Lines
crates/polars-mem-engine/src/planner/lp.rs	75.92%	13 Missing ⚠️
crates/polars-mem-engine/src/predicate.rs	40.00%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #23074      +/-   ##
==========================================
- Coverage   80.25%   80.22%   -0.04%     
==========================================
  Files        1684     1684              
  Lines      223961   223945      -16     
  Branches     2808     2808              
==========================================
- Hits       179743   179662      -81     
- Misses      43558    43623      +65     
  Partials      660      660              

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[ritchie46]

Nice, can we test it somehow?