Respect custom hyperspherical distance integration bounds

[FlorianPfaff]

Bug

AbstractHypersphereSubsetDistribution.hellinger_distance_numerical() and total_variation_distance_numerical() accepted an integration_boundaries argument and even filled in the subclass-specific full-domain default, but then discarded that value and called integrate_fun_over_domain(). Consequently, callers asking for a distance integral over a subset of the hypersphere silently received the full-domain distance instead.

Fix

Route both distance integrands through integrate_fun_over_domain_part(..., integration_boundaries), matching integrate_numerically() and preserving the existing full-domain behavior when no custom bounds are supplied.

Regression

Add two non-identical S1 von Mises-Fisher distributions and verify, for both Hellinger and total-variation distance, that a strict positive-length angular subset gives a positive distance that is strictly smaller than the full-circle distance. On current main, the partial and full values are identical because the supplied bounds are ignored.

JAX skips the numerical integration regression consistently with the existing SciPy-based hyperspherical integration limitation.

[github-actions]

✅MegaLinter analysis: Success

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✅ PYTHON	black	2033	1	0		0	65.62s
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✅ REPOSITORY	checkov	yes		no		no	35.0s
✅ REPOSITORY	git_diff	yes		no		no	0.28s
✅ REPOSITORY	secretlint	yes		no		no	107.04s
✅ REPOSITORY	syft	yes		no		no	2.85s
✅ REPOSITORY	trivy-sbom	yes		no		no	2.82s
✅ YAML	prettier	11	0	0		0	1.06s
✅ YAML	v8r	11		0		0	17.17s
✅ YAML	yamllint	11		0		0	1.0s

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