Adaptive target duration is scaled by `threads_per` worker

Adaptive Clusters with `threads_per_worker > 1` will scale to reach a duration `target_duration / threads_per_worker`:

```python
from dask.distributed import LocalCluster, Client, Adaptive
from dask import bag as db
from time import time, sleep

cluster = LocalCluster(n_workers=1, threads_per_worker=2, memory_limit=1e9)
ca = cluster.adapt(minimum=1, maximum=10, target_duration="20s", scale_factor=1)

client = Client(cluster)

start = time()
db.from_sequence((0.5 for n in range(100)), npartitions=100).map(sleep).compute()
print(time() - start, "seconds with", len(cluster.workers), "workers")
```
```
9.84409236907959 seconds with 3 workers
```

(Note that I set `scale_factor=1` to prevent anything but the target duration from scaling up the cluster.)

With single-threaded workers, we get what we desired (approx. 20s):
```python
[...]
cluster = LocalCluster(n_workers=1, threads_per_worker=1, memory_limit=1e9)
ca = cluster.adapt(minimum=1, maximum=10, target_duration="20s", scale_factor=1)
[...]
```
```
18.264188766479492 seconds with 3 workers
```

- Should this be documented?
- Or should behaviour of Adaptive be corrected to include the number of threads per worker in the estimate of the expected target duration?