Recommendation Requested / Observed mismatch

When the adaptive core gets a target value that recommends a scale-down, it always appears to take the first worker, as defined here: https://github.com/dask/distributed/blob/716d5260ce8b9f8c5e46fbdfba54f0805f40fd9a/distributed/deploy/adaptive_core.py#L192

This is because `requested` and `observed` contain completely different data.
`requested` takes the name of the worker, which is indicated by the clusters as an incremental integer.
https://github.com/dask/distributed/blob/716d5260ce8b9f8c5e46fbdfba54f0805f40fd9a/distributed/deploy/spec.py#L550

However, the `observed` names, or the names the scheduler gets from the workers, appear to be the addresses of the workers.
This results in a mismatch as the sets are compared, and as there is no overlap, the adaptive core assumes that it is still awaiting some workers, and thus can kill the not-yet-arrived workers. This is counterproductive, as this causes the adaptive algorithm to kill based on ordering, rather than idle behaviour.

**Environment**:

- Dask version: 2023.3.0
- Python version: 3.11
- Operating System: Ubuntu 22.04 (docker)
- Install method (conda, pip, source): pip