[Feature] Multi-components workload scheduling

[RainbowMango]

What is multi-components workload
Kubernetes workloads like Deployment, StatefulSet, and Pod all consist of one component(one pod template) with one or more replicas.
AI training and Big-data workloads, usually consist of more than one component, each component might have multiple replicas.

What would you like to be added:
Provide fine-grained support for workloads which usually consist of two or more components, like:

• Kubeflow Training/Big-data workloads
  • TFJob(Tensorflow Job), which may consist of multiple components like PS, Worker, Chief, Master, and Evaluator.
  • PyTorchJob which may consist of two components, like Master and Worker.
  • MXJob, which may consist of multiple components, like Scheduler, Server, Worker, TunerTracker, TunerServer, and Tuner.
  • XGBoostJob, which may consist of two components, like Master and Worker.
  • MPIJob, which may consist of two components, like Launcher and Worker.
  • PaddleJob, which may consist of two components, like Master and Worker.
  • SparkApplication, which may consist of two components, like driver and executor.
• FlinkDeployment, which may consist of two components, like jobManager and taskManager.
• Vocalno Job, which may consist of multiple tasks.
• etc

User story

Proposal
Working In Progress: #5085
Why is this needed: