### 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)

#
#
from __future__ import annotations
import jmespath
import pytest
from tests.charts.helm_template_generator import render_chart
from tests.charts.log_groomer import LogGroomerTestBase
class TestScheduler:
    """Tests scheduler."""
    @pytest.mark.parametrize(
        "executor, persistence, kind",
        [
            ("CeleryExecutor", False, "Deployment"),
            ("CeleryExecutor", True, "Deployment"),
            ("CeleryKubernetesExecutor", True, "Deployment"),
            ("KubernetesExecutor", True, "Deployment"),
            ("LocalKubernetesExecutor", False, "Deployment"),
            ("LocalKubernetesExecutor", True, "StatefulSet"),
            ("LocalExecutor", True, "StatefulSet"),
            ("LocalExecutor", False, "Deployment"),
        ],
    )
    def test_scheduler_kind(self, executor, persistence, kind):