More Polars plan optimizations for TPC-DS

[Matt711]

Description

Hand-tune polars_impl for 19 TPC-DS benchmark queries in python/cudf_polars/cudf_polars/experimental/benchmarks/pdsds_queries/. Each rewrite preserves query semantics and only changes how the polars LazyFrame is constructed; duckdb_impl is unchanged.

The optimizations apply a small set of recurring patterns that the polars optimizer does not (yet) perform automatically:

• Predicate pushdown on dimension tables — pre-filter date_dim, item, store, etc. by literal predicates (year, quarter, month window, category/class/brand) before any join, so the join builds smaller hash tables.
• Semi-join fact-table pre-filtering — use selective dimension keys (and in some cases store_returns (customer, item) pairs) as semi-join probes against the fact tables, shrinking them before the expensive joins.
• Projection pushdown — select(...) only the columns each table contributes before joining, instead of relying on the planner to prune them later.
• Condition-join → equi-join — replace cross-join + filter and CONDITIONALJOIN-style patterns with constant-key equi-joins where the predicate is equivalent.
• Single-pass bucket aggregation — collapse multiple independent global-sum group-bys over the same fact table into one pass that emits the values in a single aggregation, replacing N scans with 1.
• Join reordering — defer non-selective joins (e.g. customer) until after the selective filter chain so the row count entering the deferred join is much smaller.

Test plan

• Run TPC-DS validation against DuckDB on the 19 modified queries
• Run benchmark sweep and confirm no regressions vs. main on unmodified queries
• Confirm result equality (sorted output) matches DuckDB reference

Checklist

• I am familiar with the Contributing Guidelines.
• New or existing tests cover these changes.
• The documentation is up to date with these changes.

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