[Data] Introduce seams to DefaultAutoscaler2 to make it more testable

[400Ping]

Description

The DefaultAutoscaler2 implementation needs an AutoscalingCoordinator and a way to get all of the _NodeResourceSpec.

Currently, we can't explicitly inject fake implementations of either dependency. This is problematic because the tests need to assume what the implementation of each dependency looks like and use brittle mocks.

To solve this:

• Add the FakeAutoscalingCoordinator implementation to a new fake_autoscaling_coordinator.py module (you can use the code below)
• DefaultClusterAutoscalerV2 has two new parameters autoscaling_coordinator: Optional[AutoscalingCoordinator] = None and get_node_counts: Callable[[], Dict[_NodeResourceSpec, int]] = get_node_resource_spec_and_count. If autoscaling_coordinator is None, you can use the default implementation.
• Update test_try_scale_up_cluster to use the explicit seams rather than mocks. Where possible, assert against the public interface rather than implementation details

Related issues

Closes #59683

[gemini-code-assist]

Code Review

This pull request refactors DefaultClusterAutoscalerV2 to support dependency injection for its autoscaling coordinator and node count retrieval logic, making it more testable. It introduces a new FakeAutoscalingCoordinator for testing purposes, which always allocates requested resources. Correspondingly, the tests for DefaultClusterAutoscalerV2 are updated to utilize this new injection mechanism and the fake coordinator, replacing previous mocking. Review comments suggest minor code style improvements, including using the or operator for concise default value assignment in DefaultClusterAutoscalerV2's __init__ method and dict.pop() for more concise item removal in FakeAutoscalingCoordinator.

[400Ping]

@bveeramani PTAL