### 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)

"nodeSelector": {"type": "not-me"},
            },
            show_only=["templates/scheduler/scheduler-deployment.yaml"],
        )
        assert expected_affinity == jmespath.search("spec.template.spec.affinity", docs[0])
        assert "ssd" == jmespath.search(
            "spec.template.spec.nodeSelector.type",
            docs[0],
        )
        tolerations = jmespath.search("spec.template.spec.tolerations", docs[0])
        assert 1 == len(tolerations)
        assert "dynamic-pods" == tolerations[0]["key"]
        assert expected_topology_spread_constraints == jmespath.search(
            "spec.template.spec.topologySpreadConstraints[0]", docs[0]
        )
    def test_should_create_default_affinity(self):
        docs = render_chart(show_only=["templates/scheduler/scheduler-deployment.yaml"])
        assert {"component": "scheduler"} == jmespath.search(
            "spec.template.spec.affinity.podAntiAffinity."
            "preferredDuringSchedulingIgnoredDuringExecution[0]."
            "podAffinityTerm.labelSelector.matchLabels",
            docs[0],
        )
    def test_livenessprobe_values_are_configurable(self):
        docs = render_chart(
            values={
                "scheduler": {
                    "livenessProbe": {
                        "initialDelaySeconds": 111,
                        "timeoutSeconds": 222,