TreeRegressors with MSE Criterion do not correctly handle missing-values

### Describe the bug

I found this bug when analyzing the PR/issue from #28295 and working on #27966.

Essentially, this bug is only found in `RegressorCriterion` because there one handles additionally the square of the `y` variables encoded in the variable `sq_sum_total`. However, this does not handle correctly when missing values are sent to the left node, even though the `sum_left` and `sum_right` are handled correctly.

The source of this issue lies in the `children_impurity` [function](https://github.com/scikit-learn/scikit-learn/blob/6e918a1f5fac2b13a9b387710980820734243bb9/sklearn/tree/_criterion.pyx#L1153-L1161).

The following fix should be valid:

```python
        for p in range(start, pos):
            i = sample_indices[p]

            if sample_weight is not None:
                w = sample_weight[i]

            for k in range(self.n_outputs):
                y_ik = self.y[i, k]
                sq_sum_left += w * y_ik * y_ik
     
        # added lines of code necessary to compute correct missing value 
        if self.missing_go_to_left:
            for p in range(end_non_missing, self.end):
                i = sample_indices[p]
                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    y_ik = self.y[i, k]
                    sq_sum_left += w * y_ik * y_ik
        
        # continue with the rest of the function
        sq_sum_right = self.sq_sum_total - sq_sum_left
```

However, this issue is quite deep because it not only affects MSE, but also any Regression Criterion that requires an additional number to compute the relevant Criterion. 

I am happy to submit a PR with some relevant tests. This is quite an interesting issue because it doesn't seem to "affect" performance of `DecisionTreeRegressor` in unit-tests, but intuitively I would've expected it to. It's possible that we might even see a performance increase in MSE. Note that the fix will also help in the PR https://github.com/scikit-learn/scikit-learn/pull/27966 because I surmise it will improve performance of ExtaTrees

### Steps/Code to Reproduce

```python
# With this dataset, the missing values will always be sent to the left child
# at the first split. The leaf will be pure.
X = np.array([np.nan, 2, np.nan, 4, 5, 6]).reshape(-1, 1)
# X = np.array([np.nan, 2, 3, 4, 5, 6]).reshape(-1, 1)
y = np.arange(6)

tree = DecisionTreeRegressor(random_state=2, max_depth=1).fit(X, y)

from sklearn.tree import plot_tree
dtree = plot_tree(tree)
```


### Expected Results

![image](https://github.com/scikit-learn/scikit-learn/assets/3460267/54600cc6-f622-4acc-87e2-8dea33cd1b73)


### Actual Results

![image](https://github.com/scikit-learn/scikit-learn/assets/3460267/0749cda5-6c3c-4d18-a680-e2783d3d06fa)


### Versions

```shell
1.5.dev0
```

COMMENT:
Perhaps two separate issues:

1. Feature importances

To compute the feature importances and visualization, I agree the `improvement` variable is the one: The improvement is a function of the impurity_left and impurity_right, so if those are in fact incorrect, the improvement is not computed correctly: https://github.com/scikit-learn/scikit-learn/blob/6e918a1f5fac2b13a9b387710980820734243bb9/sklearn/tree/_splitter.pyx#L533-L540

2. Using the impurity value

But in the [tree](https://github.com/scikit-learn/scikit-learn/blob/6e918a1f5fac2b13a9b387710980820734243bb9/sklearn/tree/_tree.pyx#L334-L357), the `impurity_right/left` is pushed onto the stack, which is used as `impurity` in the next children nodes as the "parent impurity". This "parent impurity" is used as a check and input for many things, so wouldn't it affect the building of the tree?

Labels: Bug