feat(runner): Monte Carlo runner, closed-form metrics and balance CLI

Adds the S6 layer on top of the deterministic combat engine:
- metrics.py: closed-form binomial statistics (win rate, standard error,
  3-sigma confidence band) with clamped degenerate proportions.
- runner.py: EncounterSpec/Side definitions, per-run SeededRng streams,
  global duplicate-id disambiguation, attrition averages per combatant.
- cli.py: pf1e-sim entry point producing a French balance report with
  win rates, 3-sigma bands, draws, rounds, and attrition.
- Movement fix: greedy straight-line heuristic could oscillate at walls;
  _step_toward now follows the true shortest path via an unbounded
  Dijkstra cost field from the target (Grid.reachable budget=None).
- Regression tests: melee unit routes around a wall and engages; grid
  unbounded-budget coverage.

tests/test_grid.py

@@ -112,3 +112,9 @@ def test_reachable_cannot_enter_wall_squares() -> None:
     grid = make_grid(["..", ".C"])
     reached = grid.reachable((0, 0), 9, frozenset())
     assert (1, 1) not in reached
+
+
+def test_reachable_unbounded_budget_covers_whole_map() -> None:
+    grid = make_grid([".#.", "..."])
+    reached = grid.reachable((0, 0), None, frozenset())
+    assert reached == {(0, 0): 0, (1, 0): 1, (1, 1): 2, (1, 2): 3, (0, 2): 4}