Memory leak when submitting futures

I'm new to dask and trying to parallel some calculations on HPC cluster using `SLURMCluster`. Apparently, I faced a memory leak similar to that discussed in #5960. There is a reproducer of code I run (the only difference is that in real workflow I submit scipy.optimize.minimize function with some args, then get result and save it to file).

```python
from memory_profiler import profile

from dask_jobqueue import SLURMCluster
from dask.distributed import Client, as_completed

@profile
def big_arr():
return [5]*(10**5)

@profile
def go():

extra_directives = [f"-J check_mem", f"-o daskout", f"-e daskerr"]
cluster = SLURMCluster(
queue='mpi', # your queue
account="tguskov", # your account
cores=1,
processes=1,
memory="1GB",
job_name='check_mem',
walltime='06:00:00',
job_extra_directives=extra_directives, #_directives
)
client = Client(cluster)

client.cluster.scale(5)
futures = []
for i in range(10):
futures.append(client.submit(big_arr))

for future in as_completed(futures):
sol = future.result()
del future, sol

client.cluster.scale(0)

del futures, client, cluster

for i in range(4):
go()
```

There is also a graph of `mprof` memory usage by the script above:

It's clear for me from that graph that smth's wrong with submitting a new future (10 peaks corresponding to 10 submitted futures on each iteration). Creating`SLURMCluster` outside of the function `go()` doesn't make any difference. The lines with aggressive deletion are also doesn't really affect the memory usage.

Here I used `dask 2022.1.1` and `distributed 2022.1.1` according to temporary solution from #5960, but that didn't work out for me either. The same situation with the latest(`2024.8.0`) version of `dask` and `distributed`.

The memory leak is around a few GB per iteration in my original case where I try to submit around ~500 futures on 100 workers.

I'm also providing my bash script just in case:

```shell
#!/bin/sh
#SBATCH -p mpi
#SBATCH -N 1
#SBATCH -n 1
#SBATCH --mem=1GB
#SBATCH -o output
#SBATCH -e errors

ls -l|grep mprofile|xargs rm -f --

conda activate dask
mprof run --multiprocess --python python check.py # --include-children
```

**Env**

`dask==2022.01.1/2024.8.0`
`distributed==2022.01.1/2024.8.0`
`dask_jobqueue==0.8.5`