`n_jobs` support details in docs

[Alexsandruss]

Description

Adds a doc page for n_jobs specifics of sklearnex.

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[david-cortes-intel]

Thanks for adding these explanations. But it's still missing important pieces of information and leaves several questions unanswered:

• It's missing the threading part of MKL, the static linkage part, and how it interacts with environment variables, n_jobs parameter, inner_max_num_threads parameter, and threadpoolctl configurations.
• It doesn't mention how the threading works when put under a threadpoolctl context.
• The explanation is unclear about what ends up happening with the number of threads when using environment variables in addition to passing n_jobs as parameter.
• Could mention what happens with n_jobs in GPU mode.
• There's a difference in the threading configuration logic between daal4py and sklearnex, which this doc could also mention.
• It doesn't cover the part about some configurations being global, which is quite relevant when using python-based multi-threading.
• It could mention that the TBB threading doesn't automatically avoid nested parallelism when used in conjunction with OpenMP (which sklearn uses) and/or with joblib or python threads.
• Some estimators perform better when not using all threads - for example, linear regression is faster on LNL laptops when not using low-power E-cores. Perhaps could mention these sort of things here as they are relevant.

[icfaust]

@Alexsandruss make sure to merge main for latest CI checks on docs

[david-cortes-intel]

Closing in favor of https://github.com/uxlfoundation/scikit-learn-intelex/pull/2768