Yeo-Johnson Power Transformer gives Numpy warning (and raises scipy.optimize._optimize.BracketError in some cases)

### Describe the bug

When I use a power transformer with yeo-johnson method I get this warning in numpy:

`../lib/python3.10/site-packages/numpy/core/_methods.py:235: RuntimeWarning: overflow encountered in multiply`

Strangely I can't surpress this warning with filter.warnings()

**Tl;dr: The eroor seems to appear when multiple instances of the transformer are called, in this example because n_jobs=2 is set in the preprocessor. But the bug also appears with n_jobs=1 when you call this preprocessor in a grid search for example.**

### Steps/Code to Reproduce

The following code snippet will raise a warning:

```python
import numpy as np
from sklearn.preprocessing import PowerTransformer

rng = np.random.default_rng(0)
X = rng.exponential(size=(50,))
X -= X.max()
X = -X
X += 100
X = X.reshape(-1, 1)

method = "yeo-johnson"
transformer = PowerTransformer(method=method, standardize=False)
transformer.fit_transform(X)
```

### Expected Results

No Warnings

### Actual Results
```pytb
/Users/glemaitre/mambaforge/envs/dev/lib/python3.8/site-packages/numpy/core/_methods.py:233: RuntimeWarning: overflow encountered in multiply
  x = um.multiply(x, x, out=x)

```

### Versions

```shell
System:
    python: 3.8.12 | packaged by conda-forge | (default, Sep 16 2021, 01:38:21)  [Clang 11.1.0 ]
executable: /Users/glemaitre/mambaforge/envs/dev/bin/python
   machine: macOS-12.3.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.2.dev0
          pip: 21.3
   setuptools: 58.2.0
        numpy: 1.21.6
        scipy: 1.8.0
       Cython: 0.29.24
       pandas: 1.4.2
   matplotlib: 3.4.3
       joblib: 1.0.1
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /Users/glemaitre/mambaforge/envs/dev/lib/libopenblas_vortexp-r0.3.18.dylib
        version: 0.3.18
threading_layer: openmp
   architecture: VORTEX
    num_threads: 8

       user_api: openmp
   internal_api: openmp
         prefix: libomp
       filepath: /Users/glemaitre/mambaforge/envs/dev/lib/libomp.dylib
        version: None
    num_threads: 8
```

COMMENT:
> Can someone craft a minimal reproducer with a single column dataset, possibly syntactically generated?

Here is a minimal reproducer, but you have to use this dataset:

[dataset.zip](https://github.com/scikit-learn/scikit-learn/files/8662083/dataset.zip)

```
from joblib import load
import pandas as pd
from sklearn.preprocessing import PowerTransformer


data = load("X_train.joblib")
data = pd.DataFrame(data["YearModernization"])

pow_features = ["YearModernization"]

#Method 1 - Works fine
pt = PowerTransformer(method="yeo-johnson")
pt.fit(data)
print("This has worked without error")

#Method 2 - Error warning because n_jobs>1

power_transformer = Pipeline(steps=[('pow', PowerTransformer(method="yeo-johnson"))])

# Preprocessing the dataframe
preprocessor = ColumnTransformer(
    transformers=
    [   
       ("pow", power_transformer, pow_features)
    ], n_jobs=-1
)
# Pipeline for fitting
preprocessor.fit(data)
print("This produces an error if n_jobs is set >= 1")
```

Labels: Bug, module:preprocessing