utils_tests `cleanup` fixture does not play well with `threading`

**What happened**:

I use the `distributed.utils_test` fixture `cleanup` in my pytest tests. I have some code that uses dask `threading` scheduler. However, the `cleanup` fixture has a `check_thread_leak` function which throws an error.

When I took a deeper look, there are leftover threads such as `['ThreadPoolExecutor-1_0', 'ThreadPoolExecutor-2_0', 'ThreadPoolExecutor-2_1', 'ThreadPoolExecutor-2_2', 'ThreadPoolExecutor-2_3']` which were started by the dask thread executor.

In the thread pool executor, there is a shutdown routine scheduled using `atexit`:
```python
#.../lib/python3.8/site-packages/dask/threaded.py(70)get()
#-> atexit.register(default_pool.shutdown)
```
which only gets run when the interpreter shuts down. This is after the `cleanup` fixture checks for leaked threads, which is what I assume is the root of this bug.

**What you expected to happen**:

I expect the `cleanup` fixture to finish without throwing an error.

**Minimal Complete Verifiable Example**:

```python
# cleanup_throws.py
import pytest
import dask
from dask.system import cpu_count
from distributed import Client
from distributed.utils_test import cleanup
from distributed.utils_test import cluster, loop

CLUSTER_SESSION_NWORKERS = cpu_count()

@pytest.fixture
def cluster2(loop):
with cluster(nworkers=CLUSTER_SESSION_NWORKERS) as (scheduler, workers):
yield (scheduler, workers)

@pytest.fixture
def client(loop, cluster2):
scheduler, workers = cluster2
with Client(scheduler["address"], loop=loop) as _client:
yield _client

@pytest.mark.parametrize(
"scheduler",
["threads"],
)
def test_something(client, scheduler):
ddf = dask.datasets.timeseries()

ddf = ddf[ddf.y > 0]
ddf = ddf.groupby("name").x.std()

ddf.compute(scheduler=scheduler)

# Run with `pytest
```

**Anything else we need to know?**:

**Environment**:

- Dask version: 2022.6.0, latest `main` both have this issue
- Python version: 3.8.13
- Pytest version: 7.1.2
- Operating System: macOS Big Sur 11.6
- Install method (conda, pip, source): `conda`