Fix ConvergenceWarning in `plot_gpr_on_structured_data.py` example

This issue is about addressing a `ConvergenceWarning` that occurs when running the `examples/gaussian_process/plot_gpr_on_structured_data.py `example in CI (also when building the documentation locally).

The example creates three plots. The last use case on a classification of DNA sequences throws a `ConvergenceWarning` related to the `baseline_similarity_bounds` defined in a custom kernel when fitting. It seems that the lower bound is pushed resulting in the lack of convergence.

This occurs with the setting `baseline_similarity_bounds=(1e-5, 1))` in the custom kernel.

Even setting `baseline_similarity_bounds=(1e-40, 1)) ` results in the same warning:
```
ConvergenceWarning: The optimal value found for dimension 0 of parameter baseline_similarity is close to the specified lower bound 1e-40. Decreasing the bound and calling fit again may find a better value.
```

Lowering the bound further with `baseline_similarity_bounds=(1e-50, 1)) ` results in a different warning stemming from `lbfgs`:
```
ConvergenceWarning: lbfgs failed to converge (status=2): ABNORMAL: .
Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html
```

It would be preferable to resolve this so the example can be build without displaying warnings.


While being at the example, other small improvements are welcome (for instance fixing the typo in "use of kernel functions that operates" (the s in operates)).

COMMENT:
Thanks for investigating @EngineerDanny.

> Upon looking into this warning, I thought maybe then increasing the max_iter_predict in
gp = GaussianProcessClassifier(kernel, max_iter_predict=500_000_000) will fix it, but max_iter_predict does not control the iterations of the optimizer at all. So I looked at the optimizer code here [_lbfgsb_py.py](https://github.com/scipy/scipy/blob/7ecbcb8c2ebcbb8c87d2fc98a98bbc9f7e34f497/scipy/optimize/_lbfgsb_py.py#L92), and I found that the default value of the max_iter is 15000. This means that the only way to solve the issue then is to create a custom optimizer with the required max_iteration and put it in gp = GaussianProcessClassifier(kernel=kernel, optimizer=custom_optimizer).

For reference, I have tried to change the code of `_BinaryGaussianProcessClassifierLaplace` to see if setting a higher value of the internal lbfgs' `maxiter` could fix the problem on this particular example and I still get an ABNORMAL status from `lbfgs` and furthermore it arrives at iteration 2, so the default value of `maxiter=15000` is not the problem (the message output by `_check_optimize_result` is misleading in this case).

So the problem is that lbfgs fails to optimize the custom kernel hyperparameter. This example was introduced in @yhtang's #15557. @yhtang do you have any idea why the hparam optimization problem is ill-defined for this kernel?

I am not sure this warning is caused by a bug in `GaussianProcessClassifier/Regressor`, or in the custom kernel code (e.g. could be fixed by a change of parametrization), or if this is a fundamental limitation of this kernel. If the latter, disabling hparam tuning by setting the `baseline_similarity_bounds` to `"fixed"` would be the correct solution, but it would be great to be able to document why it's the case in the example (and the test) as suggested by @StefanieSenger above.

Labels: help wanted, Needs Investigation