ENH: seed Monte Carlo per simulation index for worker-invariant inputs

[thc1006]

This makes Monte Carlo input sampling reproducible regardless of how the run is executed. Today the stochastic models are seeded once per worker (parallel) or once before the loop (serial), so the inputs you get depend on the worker count and on which worker draws which simulation. Two runs with the same seed but different worker counts produce different inputs.

The change seeds per simulation index instead. From the run's root seed I spawn one SeedSequence child per index, and reseed the models from child_seeds[i] before simulation i. SeedSequence.spawn is invariant to the spawn count, so index i always maps to the same child, including in append mode. Each index seed is then split three ways so the environment, rocket, and flight don't share a draw stream.

After this, the generated inputs are identical across serial, parallel(2), and parallel(N) for a fixed seed, and a split append run (3, then +3) matches a single run of 6.

Behavior changes worth calling out

Both shift the numbers a fixed seed produces, so any stored baseline regenerates:

1. The env/rocket/flight decorrelation. They used to share one seed, so their first draws were correlated.
2. The serial index field now counts from 0, matching the parallel path, which already did.

Tests

New tests/unit/simulation/test_montecarlo_determinism.py: serial reproducibility, worker-invariance (serial == parallel(2) == parallel(4), bit-identical per index), append reproducibility, and the None-seed path. They stub Flight so only input sampling runs, and drop the one stdlib-random thrust_source so the inputs are byte-reproducible from the numpy seed alone. Marked slow (each sim rebuilds a rocket), matching the existing test_monte_carlo_simulate.

Locally: ruff and pylint both 10/10, and the four tests pass with --runslow.

Known gap

List-valued stochastic attributes are drawn with the stdlib random.choice (a global, unseeded generator), so a model with multi-element lists isn't fully worker-invariant yet. BPC isn't affected (its lists are single-element). A general fix would seed the stdlib random per index too, or move those draws onto numpy. I'll note it upstream when this goes to RocketPy.

Unrelated, but flagging it since I ran the suite: test_monte_carlo_simulate is already failing on develop (processed_results has no "apogee", and the sims themselves don't error), independent of this change. Happy to look at that separately if it's useful.