[EPIC] WorkspaceKind Overview (Admin)

### Certification

- [x] I certify I am an Epic Owner for Kubeflow Notebooks 2.0 and expected to create planning-related issues.

### User Story

As an ML Platform Admin, Joel needs visibility into how compute resources are assigned across the cluster
without requiring metrics infrastructure or time-series databases. While real-time utilization metrics
(https://github.com/kubeflow/notebooks/issues/307, https://github.com/kubeflow/notebooks/issues/542)
provide deeper insight, they require additional infrastructure (Metrics Server, Prometheus, etc.) that may
not be available in all deployments.

By aggregating information already present in Workspace and WorkspaceKind custom resources, Joel can
understand the current state of resource assignment—who has been allocated what CPU, memory, and GPU
resources—enabling him to:

- Identify which users and namespaces have the largest resource allocations
- Understand distribution of resource assignments across different WorkspaceKinds and podConfigs
- Spot potential imbalances or capacity concerns before they become problems
- Make informed decisions about resource quotas and WorkspaceKind configurations

This approach provides immediate value with minimal implementation complexity, leveraging data that already
exists in the system.

Start by reviewing the Workspace and WorkspaceKind custom resources and the related metrics issues linked in the user story. Define how CPU, memory, and GPU assignments should be aggregated by users, namespaces, WorkspaceKinds, and podConfigs without requiring Metrics Server or Prometheus. Done means the planned admin overview exposes those resource allocations and distribution details.

Written by the indexing model from the issue text.