Qualitative tests for mesa and mesa-frames benchmarks

Comparability between `mesa `and `mesa-frames `models are crucial for validating the correctness of model implementations.
To achieve this given the same seed and initial conditions, we need:
1. A compatibility layer that transforms a `mesa.Model `into a `mesa-frames.ModelDF `(this can be implemented relatively easily, I have already done some work on a local branch)
2. Consistent randomized operations (also including shuffling of agents) across frameworks. This is more challenging because of the different random number generators (`mesa `uses `random.Random `and `mesa-frames `uses `numpy.random.Generator`) and different shuffling (`mesa `uses `random.Random.shuffle` and `mesa-frames` uses the native DFs shuffle operation). Maybe with an appropriate decorator, we could substitute random operations for mesa-frames models at runtime, at the cost of performance but gaining the reproducibility.

Start by reviewing the existing mesa.Model and mesa-frames.ModelDF implementations, along with the compatibility work mentioned in the issue. Compare random.Random and numpy.random.Generator behavior, including agent shuffling, under identical seeds and initial conditions. Done means qualitative benchmark tests demonstrate comparable results or clearly document the remaining differences.

Written by the indexing model from the issue text.