Keep spans with unknown inputs out of Model Error

Reproduced: a window unknown for a whole 15-minute span was replayed as
closed and scored as a Model Error sample (MAE 0.76 °C on one sample).

- A zone's replay is complete only if every step's model inputs were
  observed: window openings (and a confirmed recovery), the wind, direction
  and other zones' windows that an open window's terms use, and, for the
  extended layer, electrical heat and neighbours, whose own replay must be
  complete too.
- Incomplete spans are counted, not scored; the base model's error on them
  is kept separately (it needs only the relay and outdoor temperature).
  Spans that could not be replayed (uncertain radiator lag, no reading at
  the end) are counted as skipped. Model Error sensors show incomplete,
  skipped and incomplete_base_model_mae; stored data gains an "excluded"
  section and older data loads unchanged.
- Window inputs no longer stand in for unknown values: the facing term is
  unknown while a window is open and the wind direction is not, and the
  cross-ventilation input is unknown while another zone's window is. With
  every window closed, opening terms are 0 and need no weather input.
  Learning refuses such samples; forecasts still assume calm air.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

tests/ha/test_lifecycle.py

@@ -302,6 +302,7 @@ async def test_forecast_error_sensors_score_scheduled_polls(hass, entry, inputs,
     assert state.attributes["mae"] == accuracy["mae"] > 0
     assert state.attributes["quality"] == "insufficient"
     assert state.attributes["samples"] == 1
+    assert (state.attributes["incomplete"], state.attributes["skipped"]) == (0, 0)
     assert state.attributes["zones"]["room"]["samples"] == 1  # by zone name
     assert state.attributes["unit_of_measurement"] == "°C"
     assert state.attributes["friendly_name"] == "Heating MPC Model Error 15 min"