V1.5 randomly hangs on import

### Describe the bug

Since upgrading to version 1.5.0 we are seeing issues where the process stalls on import. Through a core dump, it appears to be locking up [here](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/__init__.py#L154). We run hundreds of processes in parallel and it will hang on only a handful (less than 10).

### Steps/Code to Reproduce

```
import sklearn
```

### Expected Results

it imports

### Actual Results

process hangs indefinitely.

### Versions

```shell
System:
    python: 3.11.3 (main, May 15 2023, 15:45:52) [GCC 11.2.0]
executable: /home/jcoder/git/neo/neo/pyenv/bin/python
   machine: Linux-6.1.87-x86_64-with-glibc2.31

Python dependencies:
      sklearn: 1.5.0
          pip: 24.0
   setuptools: 70.0.0
        numpy: 1.24.4
        scipy: 1.11.1
       Cython: 0.29.35
       pandas: 2.0.2
   matplotlib: 3.7.2
       joblib: 1.3.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /opt/anaconda/2023/envs/pandas2/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: Zen
    num_threads: 64

       user_api: openmp
   internal_api: openmp
         prefix: libgomp
       filepath: /home/~~~~~~/lib/python3.11/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
    num_threads: 64
```

COMMENT:
> Just to make sure I follow, you mean self._name in self._handle = _dlopen(self._name, mode) (last line of the stack-trace) right?
Correct. 

Environment is created with a base python environment with common dependencies that is shared by many users (/opt/anaconda/2023/envs/pandas2) and then users build a virtualenv in their home directory for each project. The `~~~` is to hide this info, so it would be something like `/home/<username>/<path>/<to>/<project>/pyenv` 

I've had some moderate success in recreating the issue. First, I created a job definition that just does
```python
import sklearn
```
In that setup, I see about 1/200 processes hang.
I then changed it to
```python
from threadpoolctl import ThreadpoolController
_threadpool_controller = ThreadpoolController()
```
And saw similar results, where I would see one of the processes hang, this was with threadpoolctl version 3.1.0
I then upgraded to threadpoolctl version 3.5.0 and ran the same tests and still saw processes hang (3 out of 600).
One other thing to mention is that the New Relic python agent has threads running as well and it kind of appears to be the case that if I disable New Relic, the problem goes away. I _think_ New Relic does something in their agent where they try and collect package information so I'm curious if this is in some way causing a deadlock.

Additionally, I am able to ssh into the node and run `gdb`. If I dig a little deeper, it seems `dl_open` is stucke here:
`#1  0x00007f4217386131 in pthread_mutex_lock () from /lib/x86_64-linux-gnu/libpthread.so.0`
but I am rather unfamiliar with `gdb` so if we can get infomation that would be helpful, please let me know what commands might be good to run.

Labels: Bug, Needs Reproducible Code