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Speed up device_scheduler with a recursive stock balance

`_get_stock_change` expressed a device's stock at step j as a running sum
over all earlier steps, so the number of model nonzeros grew quadratically
with the scheduling horizon (e.g. ~12k nonzeros at 12h, ~349k at 3 days).
That dominated solve time on longer horizons.

Introduce an explicit `device_stock` variable coupled by a recursive
`device_stock_balance` constraint (stock[j] = a*stock[j-1] + b*change[j],
with a, b the per-step loss coefficients that apply_stock_changes_and_losses
computes). This is mathematically equivalent -- verified identical
objectives across horizons and against the planning test suite -- and keeps
the number of nonzeros linear in the horizon. Solve time drops ~10x at the
2-day default horizon and ~23x at 3 days.

Found while investigating SeitaBV/ems#172.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LeqGFrHfGrBHAJyjdAyr3y

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