### Description

Introducing a startupProbe onto the airflow services would be useful for slow starting container and most of all it doesn't have side effects.

### Use case/motivation

We have an internal feature where we perform a copy of venv from airflow services to cloud storages which can sometimes take a few minutes. Copying of a venv is a metadata heavy load: https://learn.microsoft.com/en-us/troubleshoot/azure/azure-storage/files-troubleshoot-performance?tabs=linux#cause-2-metadata-or-namespace-heavy-workload.

Introducing a startupProbe onto the airflow services would be useful for slow starting container and most of all it doesn't have side effects.

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

self, dag_processor, executor, skip_dags_mount
    ):
        """
        DAG Processor can move gitsync and DAGs mount from the scheduler to the DAG Processor only.
        The only exception is when we have a Local executor.
        In these cases, the scheduler does the worker role and needs access to DAGs anyway.
        """
        docs = render_chart(
            values={
                "dagProcessor": {"enabled": dag_processor},
                "executor": executor,
                "dags": {"gitSync": {"enabled": True}, "persistence": {"enabled": True}},
                "scheduler": {"logGroomerSidecar": {"enabled": False}},
            },
            show_only=["templates/scheduler/scheduler-deployment.yaml"],
        )
        if skip_dags_mount:
            assert "dags" not in [
                vm["name"] for vm in jmespath.search("spec.template.spec.containers[0].volumeMounts", docs[0])
            ]