[ENH] expand distribution fitters module with new fitters

[patelchaitany]

Is your feature request related to a problem? Please describe.

The distfitter module only has 2 fitters (NormalFitter, MOMFitter). This limits the baselines available for
unconditional density estimation and the NaiveProbaRegressor (#1085). Key distribution families (Exponential, Uniform,Laplace, Empirical) have no dedicated fitters, and there's no generic MLE fitting path.

Describe the solution you'd like

Add 5 new distribution fitters to skpro/distfitter/:

ExponentialFitter - Exponential(rate) - MLE: rate = n / sum(x)
UniformFitter - Uniform(lower, upper) - Sample min/max
LaplaceFitter - Laplace(mu, scale) - Median + MAD (robust) or MLE
EmpiricalFitter - Empirical(spl) - Nonparametric, wraps raw samples
MLEFitter - Any scipy-backed dist - Generic MLE via scipy.stats.fit() with declarative param_map

All follow the existing BaseDistFitter contract (fit(X) - proba() - scalar BaseDistribution).

Describe alternatives you've considered

Additional context

Relates to roadmap issue #7 (baselines / unconditional density estimation)

[MoeenAsim]

@patelchaitany
Hi! I'd like to work on this enhancement. Since it includes five fitters, would you prefer a single PR implementing all of them, or would you be open to splitting it into smaller PRs?

[patelchaitany]

@MoeenAsim there is already pr raised for this, but you could look into other issues on the skpro repo