Gaussian Process Log Likelihood Gradient Incorrect

### Describe the bug

The gradient function of in the GaussianProcessRegressor Class is incorrect. This leads to inefficiencies fitting kernel (hyper) parameters.
The root of the issue is in that the gradient of the kernel function is made with respect to the log of the kernel parameter.

See the plot on the right showcasing the incorrect gradient currently being used in the optimization step:

![Image](https://github.com/user-attachments/assets/ec7f5582-b928-4738-9371-02ad1391c7c4)


To fix this apply the chain rule giving:

$\frac{\partial k(x)}{\partial \ln(x)} \frac{\partial \ln(x)}{\partial x} = \frac{\partial k(x)}{\partial x}$
$\frac{\partial k(x)}{\partial \ln(x)} \frac{1}{x} = \frac{\partial k(x)}{\partial x}$

When implementing this the correct gradient is given (middle plot).


TO REPRODUCE THIS BUG - see [my branch](https://github.com/conradstevens/scikit-learn/tree/gpr-log-likelihood-check) 
specifically: [sklearn/gaussian_process/tests/test_gpr_loglikelihood_check.py](https://github.com/conradstevens/scikit-learn/blob/gpr-log-likelihood-check/sklearn/gaussian_process/tests/test_gpr_loglikelihood_check.py)

I am happy to fix this bug by modifying the `_gpr` class's `log_marginal_likelihood` function. Keeping in mind things may get trickier when kernel functions have multiple hyper parameters (likely resulting in multiple iterations of the chain rule). 

### Steps/Code to Reproduce

TO REPRODUCE THIS BUG - see [my branch](https://github.com/conradstevens/scikit-learn/tree/gpr-log-likelihood-check) 
specifically: [sklearn/gaussian_process/tests/test_gpr_loglikelihood_check.py](https://github.com/conradstevens/scikit-learn/blob/gpr-log-likelihood-check/sklearn/gaussian_process/tests/test_gpr_loglikelihood_check.py)

### Expected Results

CRRECTION AND GROUND TRUTH TANGENTS CAN BE FOUND IN [my branch](https://github.com/conradstevens/scikit-learn/tree/gpr-log-likelihood-check) 
specifically: [sklearn/gaussian_process/tests/test_gpr_loglikelihood_check.py](https://github.com/conradstevens/scikit-learn/blob/gpr-log-likelihood-check/sklearn/gaussian_process/tests/test_gpr_loglikelihood_check.py)

### Actual Results

Logic error giving incorrect results.

### Versions

```shell
System:
    python: 3.12.5 | packaged by conda-forge | (main, Aug  8 2024, 18:31:54) [Clang 16.0.6 ]
executable: /opt/homebrew/Caskroom/miniconda/base/envs/sklearn-dev-5/bin/python
   machine: macOS-14.4.1-x86_64-i386-64bit

Python dependencies:
      sklearn: 1.8.dev0
          pip: 24.2
   setuptools: 72.2.0
        numpy: 2.1.0
        scipy: 1.14.1
       Cython: 3.0.11
       pandas: 2.2.2
   matplotlib: 3.9.2
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 10
         prefix: libopenblas
       filepath: /opt/homebrew/Caskroom/miniconda/base/envs/sklearn-dev-5/lib/libopenblasp-r0.3.27.dylib
        version: 0.3.27
threading_layer: openmp
   architecture: Nehalem

       user_api: openmp
   internal_api: openmp
    num_threads: 10
         prefix: libomp
       filepath: /opt/homebrew/Caskroom/miniconda/base/envs/sklearn-dev-5/lib/libomp.dylib
        version: None
```

COMMENT:
Wait I'm not sure, I tried to reproduce the results with the GaussianProcessClassifier but got the following:

![Image](https://github.com/user-attachments/assets/c0fdc021-180b-4b80-aaa9-691a2f58f1f6)

I probably need to look into the code a bit more, I think the _BinaryGaussianProcessClassifierLaplace enforces a constrained optimization of the kernel which is not in log-space so the gradient calculated is actually correct. I need to read the RW2006 to get a better understanding, but this may then just be specific to GPR.

Labels: Bug, Needs Investigation