`test_y_multioutput`in Gaussian Process is failing on Debian 32bit

### Describe the bug

On 32bit systems on debian the test `test_y_multioutput` is failing.

The test will probably be just skipped during the build, this is not urgent, but maybe something underlying multioutput and Gaussian Process is hidden here (see #19995).

### Steps/Code to Reproduce

```
 pytest sklearn/gaussian_process/tests/test_gpr.py -k test_y_multioutput
```

### Expected Results

The test passes.

### Actual Results

```
=================================== FAILURES ===================================
______________________________ test_y_multioutput ______________________________
    def test_y_multioutput():
        # Test that GPR can deal with multi-dimensional target values
        y_2d = np.vstack((y, y * 2)).T
    
        # Test for fixed kernel that first dimension of 2d GP equals the output
        # of 1d GP and that second dimension is twice as large
        kernel = RBF(length_scale=1.0)
    
        gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=False)
        gpr.fit(X, y)
    
        gpr_2d = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=False)
        gpr_2d.fit(X, y_2d)
    
        y_pred_1d, y_std_1d = gpr.predict(X2, return_std=True)
        y_pred_2d, y_std_2d = gpr_2d.predict(X2, return_std=True)
        _, y_cov_1d = gpr.predict(X2, return_cov=True)
        _, y_cov_2d = gpr_2d.predict(X2, return_cov=True)
    
        assert_almost_equal(y_pred_1d, y_pred_2d[:, 0])
        assert_almost_equal(y_pred_1d, y_pred_2d[:, 1] / 2)
    
        # Standard deviation and covariance do not depend on output
        assert_almost_equal(y_std_1d, y_std_2d)
        assert_almost_equal(y_cov_1d, y_cov_2d)
    
        y_sample_1d = gpr.sample_y(X2, n_samples=10)
        y_sample_2d = gpr_2d.sample_y(X2, n_samples=10)
        assert_almost_equal(y_sample_1d, y_sample_2d[:, 0])
    
        # Test hyperparameter optimization
        for kernel in kernels:
            gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
            gpr.fit(X, y)
    
            gpr_2d = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
            gpr_2d.fit(X, np.vstack((y, y)).T)
    
>           assert_almost_equal(gpr.kernel_.theta, gpr_2d.kernel_.theta, 4)
E           AssertionError: 
E           Arrays are not almost equal to 4 decimals
E           
E           Mismatched elements: 2 / 3 (66.7%)
E           Max absolute difference: 4.77758733
E           Max relative difference: 27.70946804
E            x: array([ -4.6052,  -0.3981, -11.5129])
E            y: array([  0.1724,   0.4926, -11.5129])
sklearn/gaussian_process/tests/test_gpr.py:379: AssertionError
```

### Versions

```shell
scikit-learn 1.0.2

Python dependencies:
          pip: None
   setuptools: 59.6.0
      sklearn: 1.0.2
        numpy: 1.21.5
        scipy: 1.7.3
       Cython: 0.29.24
       pandas: 1.3.5
   matplotlib: 3.5.1
       joblib: 0.17.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```
```

COMMENT:
I don't think it is linked to #19995. Currently, we consider mulitoutput such that each target is independent and indeed it is not that straightforward.

Here, the test is failing for numerical reasons when optimizing the kernel hyperparameters.

We should probably investigate that the initial kernel in both models is the same.

Labels: Bug, module:gaussian_process