Add deployment execution step between scale-up and scale-down in the
k8s CRUD engine pipeline. Parameters (number_of_deployments, replicas,
manifest_dir) flow from pipeline matrix → topology → engine step → main.py.

- Add deployment script block to steps/engine/crud/k8s/execute.yml
- Pass deployment parameters through topology execute-crud.yml
- Deployment runs after scale-up, before scale-down + delete

begin_create_or_update() returns an LROPoller that was being discarded
in scale_node_pool and _progressive_scale, allowing execution to continue
while Azure still had an operation in-progress. Subsequent scale/delete
calls were rejected with OperationNotAllowed.

Call poller.result() to block until Azure fully completes each operation
before proceeding. Aligns scale behavior with create_node_pool and
delete_node_pool which already awaited the poller.

Add comprehensive test coverage for create_deployment and
handle_workload_operations:

- test_create_deployment_success: single deployment with readiness check
- test_create_deployment_partial_success: some deployments fail, operation
  continues and reports partial success
- test_create_deployment_failure: all deployments fail
- test_multiple_deployments: verifies N deployments created sequentially
- test_handle_workload_operations: deployment command routing + kwargs
- test_handle_workload_operations_unknown_command: returns error
- test_progressive_scaling_failure: scale continues after step failure
- test_scale_down_fails_continues: delete still runs after scale-down error
- test_returns_false_early_exit: unknown operation returns False

Add create_job() to NodePoolCRUD that deploys K8s Jobs onto node pools.
Unlike deployments/statefulsets which run indefinitely, Jobs are
run-to-completion workloads — success means the pod terminated cleanly
(succeeded > 0), failure raises immediately (no self-healing).

- Add 'jobs' subcommand to handle_workload_operations() in main.py
  with --number-of-jobs and --completions args
- Add job.yml workload template with configurable completions and
  node affinity via label_selector
- Add _check_job_condition and _is_job_condition_met to
  kubernetes_client.py — checks completion_time + succeeded count
- Add wait_for_job_completed with 5-min timeout and 30s polling
- Job kind support in apply/update/delete manifest methods

Add job execution step to the k8s CRUD engine pipeline between
deployment and scale-down. Parameters (number_of_jobs, completions)
flow from pipeline matrix → topology → engine step → main.py.

- Add jobs script block to steps/engine/crud/k8s/execute.yml
- Pass number_of_jobs and completions through topology execute-crud.yml
- Jobs run after deployment, before scale-down + delete

Fixes node pool creation failure when gpu_node_pool=False by setting
gpu_profile driver to 'None' for non-GPU pools. Original logic only
set 'None' for a specific GPU VM size, leaving non-GPU pools incorrectly
set to 'Install'.

Add comprehensive test coverage for create_job and job wait_for_condition:

- test_create_job_success: single job completes successfully
- test_create_job_failure: job fails to complete
- test_create_job_partial_success: continues on individual failures
- test_job_wait_for_condition: validates _check_job_condition and
  _is_job_condition_met for 'complete' and 'failed' states
- test_wait_for_job_completed: timeout and polling behavior
- Tests cover Job-specific semantics (succeeded count, completion_time,
  failed + no active pods)