[enhancement] enforce use of sklearn's ```check_is_fitted``` before checking oneDAL support

[icfaust]

Description

As a follow-up suggestion from #2109 , lets be consistent on where we call check_is_fitted.

This is stricter than sklearn's check_estimators_unfitted as it applies to anything that uses sklearnex's dispatch.

---

Checklist to comply with before moving PR from draft:

PR completeness and readability

• [ ] I have reviewed my changes thoroughly before submitting this pull request.
• [ ] I have commented my code, particularly in hard-to-understand areas.
• [ ] I have updated the documentation to reflect the changes or created a separate PR with update and provided its number in the description, if necessary.
• [ ] Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• [ ] I have added a respective label(s) to PR if I have a permission for that.
• [ ] I have resolved any merge conflicts that might occur with the base branch.

Testing

• [ ] I have run it locally and tested the changes extensively.
• [ ] All CI jobs are green or I have provided justification why they aren't.
• [ ] I have extended testing suite if new functionality was introduced in this PR.

Performance

• [ ] I have measured performance for affected algorithms using scikit-learn_bench and provided at least summary table with measured data, if performance change is expected.
• [ ] I have provided justification why performance has changed or why changes are not expected.
• [ ] I have provided justification why quality metrics have changed or why changes are not expected.
• [ ] I have extended benchmarking suite and provided corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.