perf: default integer arrays to int32 for ~25% memory reduction

[FBumann]

Changes proposed in this Pull Request

Cut memory for internal integer arrays (labels, vars indices, _term coords) by ~25% and improve build speed by ~10-35% by defaulting to int32 instead of int64.

What changed

• linopy/constants.py: Added DEFAULT_LABEL_DTYPE = np.int32
• linopy/model.py: Variable and constraint label assignment uses np.arange(..., dtype=DEFAULT_LABEL_DTYPE) with overflow guard that raises ValueError if labels exceed int32 max (~2.1 billion)
• linopy/expressions.py: _term coord assignment and .astype(int) for vars arrays now use DEFAULT_LABEL_DTYPE
• linopy/variables.py: ffill, bfill, sanitize use DEFAULT_LABEL_DTYPE instead of astype(int) (which widened labels back to int64); Variables.to_dataframe arange uses int32
• linopy/constraints.py: Constraints.to_dataframe arange uses DEFAULT_LABEL_DTYPE
• linopy/common.py: fill_missing_coords uses int32 arange; save_join outer-join fallback uses DEFAULT_LABEL_DTYPE instead of astype(int); polars schema infers Int32/Int64 based on actual array dtype
• test/test_constraints.py: Updated dtype assertions to use np.issubdtype (compatible with both int32 and int64)
• test/test_dtypes.py (new): Tests for int32 labels, expression vars, solve correctness, and overflow guard
• dev-scripts/benchmark_lp_writer.py (new): Benchmark script supporting --phase memory|build|lp_write with --plot comparison mode

Benchmark results

Reproduce with:

# Run on master
python dev-scripts/benchmark_lp_writer.py --phase memory --model basic -o bench_master_memory.json --label "master_org"
python dev-scripts/benchmark_lp_writer.py --phase build --model basic -o bench_master_build.json --label "master_org"

# Run on this branch
python dev-scripts/benchmark_lp_writer.py --phase memory --model basic -o bench_int32_memory.json --label "int32"
python dev-scripts/benchmark_lp_writer.py --phase build --model basic -o bench_int32_build.json --label "int32"

# Plot comparisons
python dev-scripts/benchmark_lp_writer.py --plot bench_master_memory.json bench_int32_memory.json
python dev-scripts/benchmark_lp_writer.py --plot bench_master_build.json bench_int32_build.json

Memory (dataset .nbytes)

Consistent 1.25x reduction across all problem sizes (e.g. 640 MB → 512 MB at 8M vars). The labels and vars arrays shrink 50% (int64 → int32) while lower/upper/coeffs/rhs stay float64.

Build speed

Consistently ~1.1-1.35x faster across all sizes (30 iterations with GC, tight error bars). 10-20% for large models (170ms → 153ms at 8M vars), and up to 35% for small/medium models where the fixed overhead of array allocation matters more relative to total time.

Similar results on real PyPSA model.

No influence on lp-write

Checklist

• Code changes are sufficiently documented; i.e. new functions contain docstrings and further explanations may be given in doc.
• Unit tests for new features were added (if applicable).
• A note for the release notes doc/release_notes.rst of the upcoming release is included.
• I consent to the release of this PR's code under the MIT license.