…h GPUs (ray-project#59366)

This PR enables the utilization-based autoscaler to work with GPUs.

**Motivation**

The current implementation only considers CPU and memory utilization
when making scaling decisions. This is based on an outdated assumption
that GPUs are only used by actor pools, and we can use actor pool
autoscaling to trigger node scale ups.

This assumption doesn't hold anymore. To fix deadlocks,
ray-project#54902 made actor pool
autoscaling respect resource budgets. As a result, the actor pool
autoscaler can't implicitly trigger node autoscaling anymore.

**Changes**

This PR extends the autoscaler to track GPU utilization alongside CPU
and memory:

1. Extract resource utilization calculation into a dedicated abstraction
(ResourceUtilizationGauge): This separates the concern of how
utilization is measured from how it's used
for scaling decisions. The abstraction makes the autoscaler more
testable and opens the door for alternative utilization strategies
(e.g., physical vs logical utilization, different averaging windows).
2. Include GPU nodes in scaling decisions: Previously, GPU nodes were
explicitly filtered out when determining what node types exist in the
cluster. Now all worker node types are considered, allowing the
autoscaler to request additional GPU nodes when needed.
3. Add GPU utilization to the scaling threshold check: The autoscaler
now triggers scale-up when any of CPU, GPU, or memory utilization
exceeds the threshold, rather than only CPU or memory.

---------

Signed-off-by: Balaji Veeramani <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Signed-off-by: peterxcli <[email protected]>