### 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)

assert "dags" not in [vm["name"] for vm in jmespath.search("spec.template.spec.volumes", docs[0])]
            assert 1 == len(jmespath.search("spec.template.spec.containers", docs[0]))
        else:
            assert "dags" in [
                vm["name"] for vm in jmespath.search("spec.template.spec.containers[0].volumeMounts", docs[0])
            ]
            assert "dags" in [vm["name"] for vm in jmespath.search("spec.template.spec.volumes", docs[0])]
            assert "git-sync" in [
                c["name"] for c in jmespath.search("spec.template.spec.containers", docs[0])
            ]
            assert "git-sync-init" in [
                c["name"] for c in jmespath.search("spec.template.spec.initContainers", docs[0])
            ]
    def test_persistence_volume_annotations(self):
        docs = render_chart(
            values={"executor": "LocalExecutor", "workers": {"persistence": {"annotations": {"foo": "bar"}}}},
            show_only=["templates/scheduler/scheduler-deployment.yaml"],
        )
        assert {"foo": "bar"} == jmespath.search("spec.volumeClaimTemplates[0].metadata.annotations", docs[0])
    @pytest.mark.parametrize(
        "executor",
        [
            "LocalExecutor",
            "LocalKubernetesExecutor",
            "CeleryExecutor",
            "KubernetesExecutor",
            "CeleryKubernetesExecutor",
        ],
    )
    def test_scheduler_deployment_has_executor_label(self, executor):