Workers killed by signal 6/9 and timeout errors during cluster shutdown with `LocalCUDACluster`

I'm using `dask-cuda`'s `LocalCUDACluster` for GPU-based distributed computing in a Python script. While the computation completes successfully, I encounter multiple errors during the shutdown phase.

Specifically, after calling `cluster.close()` and attempting to gracefully shut down the Dask cluster, I see repeated logs like:

```
distributed.nanny - INFO - Worker process XXX was killed by signal 6
...
distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing
...
distributed.nanny - INFO - Worker process XXX was killed by signal 9
```

Additionally, I get a traceback indicating a `TimeoutError` during internal cluster state correction:

```
tornado.application - ERROR - Exception in callback ...
TimeoutError
```

And finally, a memory-related error from tcmalloc:

```
src/tcmalloc.cc:284] Attempt to free invalid pointer 0x...
```

**Environment Setup:**

- Using `LocalCUDACluster` with explicit GPU device configuration.
- Disabled Dask optimizations (`optimization.fuse.active=False`) and set conservative memory thresholds.
- Workers are configured with `device_memory_limit="80GB"` and `threads_per_worker=1`.
- Client and cluster are manually closed at the end of execution.

**Code Snippet:**

```python
dask.config.set({"optimization.fuse.active": False})
dask.config.set({
"distributed.worker.memory.target": 0.6,
"distributed.worker.memory.spill": 0.7,
"distributed.worker.memory.pause": 0.8,
"distributed.worker.memory.terminate": 0.9,
"distributed.comm.timeouts.connect": "300s",
"distributed.comm.timeouts.tcp": "300s",
"distributed.worker.daemon": False,
"distributed.nanny.timeout": "60s"
})

cluster = LocalCUDACluster(
CUDA_VISIBLE_DEVICES=cuda_devices,
device_memory_limit="80GB",
n_workers=n_workers,
threads_per_worker=1,
dashboard_address=':0',
jit_unspill=False,
silence_logs=False
)

client = Client(cluster, timeout='60s')
client.wait_for_workers(n_workers, timeout=120)

# ... computation ...

cluster.close(timeout=300)
```

**Expected Behavior:**

Graceful shutdown of workers and scheduler without force-killing or timeout errors.

**Actual Behavior:**

Workers are terminated forcefully with signals 6 and 9, followed by timeout and memory-related errors during shutdown.

**Environment:**

- Dask version: `2024.12.1`
- Dask-CUDA version: `25.2.0`
- Python version: 3.12
- OS: Linux (assumed)
- Relevant packages: `cudf`, `cupy`, `torch`, `distributed`, etc.

**Question:**

Is this expected behavior? Are there additional configurations or best practices to ensure clean shutdown of GPU clusters in Dask?

Any help or guidance would be greatly appreciated!