feat: Add month and end_time to time window configuration

Extend TimeWindow with two new optional fields:

- month (1–12): restricts the window to a start date in the given
  calendar month. Useful for seasonal schedules.

- end_time: explicit exclusive end-of-window clock time. The effective
  end is the later of end_time and start_time + duration. Primary
  motivation is DST handling — a wall-clock end_time stays correct
  across DST transitions, whereas a fixed duration does not.

Rework window semantics so date, weekday, and month restrictions apply
to the window's start date, not to every timestamp covered by the
window. This makes overnight windows (e.g. Friday 22:00 → Saturday
06:00) behave intuitively under calendar constraints; contains() now
checks both today's and yesterday's candidate start.

duration becomes optional (default 0). All start/end/duration
computations route through _window_start_end and a new
_matches_start_date helper, so contains, earliest_start_time,
latest_start_time, and available_duration stay consistent.

Dashboard: add end_time and month inputs to the time-window editor,
render the collapsed window summary as icons + pills.

Docs, OpenAPI spec, and generated config docs are regenerated for the
new fields. Tests cover month filtering, end_time boundary cases (incl.
midnight and overnight), and start-date semantics for overnight windows.

Bump version to support automatic updates in Home Assistant.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

docs/_generated/openapi.md

@@ -1,6 +1,6 @@
 # Akkudoktor-EOS
 
-**Version**: `v0.3.0.dev2610260646141478`
+**Version**: `v0.3.0.dev2610261491542381`
 
 <!-- pyml disable line-length -->
 **Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.