Differentiate between compute and network based occupancy

Occupancy is an estimation of work the scheduler assigns to every worker. We compute this value in [`Scheduler._set_duration_estimate`](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L2784-L2810) which is invoked in a couple of places

- [transition processing->memory](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L2119-L2126) (iff previously unknown duration)
- [_add_to_processing](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L7652) (i.e. whenever we assign a task to a worker)
- [`_reevaluate_occupancy_worker`](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L3043-L3044) (periodically [if scheduler CUP load allows is](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L7450-L7468))

Occupancy is measured in seconds and is calculated by summing the _expected processing time_ of all tasks assigned to a worker. At all times, the invariant `sum(ws.processing.values()) ~ ws.occupancy` should hold (modulo floating point arithmetic errors).

This _processing time_ is defined as `TaskPrefix.duration_average + get_comm_cost(TaskState, WorkerState)`, i.e. the average _compute_ duration of the `TaskPrefix` (see [`Scheduler.get_task_duration`](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L2887-L2904)) and the estimated time to transfer all dependencies that are not, yet on that worker, see [`Scheduler.get_comm_cost`](https://github.com/dask/distributed/blob/b133009cee88fd48c8a345cffde0a8e9163426a6/distributed/scheduler.py#L2865-L2885)

Occupancy is used for four purposes

- `Scheduler.total_occupancy` (sum over all workers) is used to define an adaptive target
- `Scheduler.total_occupancy` (sum over all workers) is used to estimate worker saturation
- `WorkerState.processing` to calculate the steal_time ratio in work stealing
- `WorkerState.occupancy` for making a scheduling decision in `Scheduler.worker_objective`

With the exception of the work stealing case, all other examples are very specifically referring to the number of worker threads. Worker threads do not impact network/gather data performance.

Taking `Scheduler.worker_objective` trying to calculate `start_time` as an example, the actual start time should rather be

```python
wait_time_cpu: float = ws.compute_occupancy / ws.nthreads
wait_time_transfer: float = (ws.network_occupancy + comm_nbytes) / bandwidth

start_time = max(wait_time_transfer, wait_time_cpu)
```

This would likely increase the quality of our scheduling decisions and would very clearly avoid double counting problems like https://github.com/dask/distributed/issues/7003

On top, this would add a significant observability component since we would directly visualize how much network vs compute work is expected from a worker. I could also see a ratio of the two values to be an interesting metric to track (similar to what work stealing is trying to do with the steal ratio)