Count households once: sum the weight column unweighted

[PavelMakarchuk]

Fixes #766

Summary

budget.households was the sum of squared household weights.

build_budgetary_impact summed household_weight through _sum_output_variable, which calls data[variable].sum(). Here data is the household MicroDataFrame, which is weighted by household_weight, so .sum() on the weight column is Σ w × w. On the sparse US data that is about 10⁴ times the household count, on every run. For example, state/ny 2026 reports 135,597,233,840 households.

The fix:

• _count_output_entities(simulation, entity, weight) counts entities with a plain sum of the weight column.
• build_budgetary_impact uses it for households.
• There is no aggregate fallback, because a weighted frame always holds its weight column, and the aggregate path would weight it again.

The other budget fields are not changed. They are meant to be weighted sums (Σ w × value).

Tests

• New tests/test_simulation_output_budget.py builds the budget from real MicroDataFrames, as policyengine does. The existing builder test uses plain pd.DataFrames, which is why it never caught this.
• The new test fails on main with assert 5.0 == 3.0, and it passes with the fix.
• The same test checks that baseline_net_income, tax_revenue_impact and state_tax_revenue_impact stay weighted sums.
• uv run pytest tests/ --ignore=tests/integration: 733 passed, 23 skipped.
• ruff format --check and ruff check are clean on the changed files.
• I did not run Pyright or the integration tests.

The test file is separate from test_simulation_output_builder.py so that it does not conflict with #765, which reorders that file's imports.

Downstream

policyengine-app-v2 shows the average change per household as -budget.budgetary_impact / budget.households, so that headline is about 10⁴ times too small today. Cached results keep the wrong count until they are recomputed.

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