[Repo Assist] improve(gcm): add random_seed parameter to interventional_samples, counterfactual_samples, and average_causal_effect

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🤖 This is an automated PR from Repo Assist.

Problem

interventional_samples(), counterfactual_samples(), and average_causal_effect() in dowhy/gcm/whatif.py offered no way to control the random seed. Calls to the same function with the same inputs produced different results each run, making experiments non-reproducible. This was reported in issue #1307.

Solution

Added an optional random_seed: Optional[int] = None parameter to all three public functions. When a seed is provided, set_random_seed(random_seed) is called at the start of the function before any stochastic sampling takes place. The default None preserves the existing behaviour exactly.

# Before: non-reproducible
samples = gcm.interventional_samples(causal_model, {'X': lambda x: 1.0}, num_samples_to_draw=1000)

# After: reproducible with a seed
samples = gcm.interventional_samples(causal_model, {'X': lambda x: 1.0}, num_samples_to_draw=1000, random_seed=42)
```

The same pattern works for `counterfactual_samples` and `average_causal_effect`.

## Tests

Added two tests to `tests/gcm/test_whatif.py`:
- `test_interventional_samples_with_random_seed_is_reproducible`
- `test_counterfactual_samples_with_random_seed_is_reproducible`

Both confirm that the same seed produces bit-identical results across two independent calls.

## Test Status

```
tests/gcm/test_whatif.py: 17 passed (all existing + 2 new)

No new lint errors introduced (3 E501 violations are pre-existing in test_whatif.py).

Closes #1307

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