[ENH] Zero-Inflated Poisson and Negative Binomial shorthand distributions

[MurtuzaShaikh26]

Reference Issues/PRs:
Contributes to #554.

What does this implement/fix?

Adds two shorthand distribution classes for commonly used zero-inflated distributions:

• [ZIPoisson] — Zero-Inflated Poisson, equivalent to [ZeroInflated(p=p, distribution=Poisson(mu=mu))]
• [ZINB] — Zero-Inflated Negative Binomial, equivalent to [ZeroInflated(p=p, distribution=NegativeBinomial(mu=mu, alpha=alpha))]
  These internally delegate to the existing [ZeroInflated] compositor (merged in [ENH] zero-inflated distribution #648), providing a cleaner API for two of the most common zero-inflated distribution families used in intermittent demand forecasting.
  Files:
• New: [skpro/distributions/zi_poisson.py](
• New: [skpro/distributions/zi_negative_binomial.py]
• Modified: [skpro/distributions/init.py] — added exports

Does your contribution introduce a new dependency? If yes, which one?
My contribution does not add a new dependency.

What should a reviewer concentrate their feedback on?

• Whether the internal delegation pattern (composing via [ZeroInflated] property is appropriate vs using [_DelegatedDistribution].
• Whether additional [get_test_params] configurations are needed.

Did you add any tests for the change?
Both classes implement [get_test_params] with scalar and 2D array parameter sets, so they are automatically picked up by the existing [test_all_distrs.py] contract test suite.

Any other comments?
The [_DelegatedDistribution] base class was initially considered (as suggested in the issue), but it doesn't delegate [pmf]/[log_pmf] methods - only [pdf]/[log_pdf]. Since these are discrete distributions, direct BaseDistribution subclassing with internal delegation was used instead.

PR checklist:
For all contributions

• I've added myself to the list of contributors with any new badges I've earned :-)
• The PR title starts with either [ENH], [MNT], [DOC], or [BUG].
  For new estimators
• I've added the estimator to the API reference - in docs/source/api_reference/taskname.rst, follow the pattern.
• I've added one or more illustrative usage examples to the docstring, in a pydocstyle compliant Examples section.
• If the estimator relies on a soft dependency, I've set the python_dependencies tag and ensured dependency isolation. (N/A- no soft dependencies)

[marrov]

Closing this as there's already an open PR (#713) to solve the underlying issue.