## Problem
The helm chart does not implement a way for SequentialExecutor Airflow 2 deployments to serve logs; without using elasticsearch.

## Details
Prior implementations utilize the CLI function `serve_logs`. This function has been deprecated as of v2.

In `airflow/templates/scheduler/scheduler-deployment.yaml`:
```yaml
    181 {{- if and $local (not $elasticsearch) }}
    182         # Start the sidecar log server if we're in local mode and
    183         # we don't have elasticsearch enabled.
    184         - name: scheduler-logs
    185           image: {{ template "airflow_image" . }}
    186           imagePullPolicy: {{ .Values.images.airflow.pullPolicy }}
    187           args: ["serve_logs"]
```

This will cause the helm deployment to break; and the scheduler will perpetually fail to start the `scheduler-logs` container inside of the scheduler deployment.

Snippet from airflow [upgrade guide](https://airflow.apache.org/docs/apache-airflow/stable/upgrading-to-2.html).

```
Remove serve_logs command from CLI

The serve_logs command has been deleted. This command should be run only by internal application mechanisms and there is no need for it to be accessible from the CLI interface.
```

## Partial Solution
Not sure how the non-elastic method for serving logs going forward.

Astronomer branches yaml by:
```yaml
{{- if semverCompare ">=1.10.12" .Values.airflowVersion }}
...
{{- else }}
...
{{- end }}
```
airflow/cli/cli_parser.py
ARG_ACCESS_LOGFILE = Arg(
    ("-A", "--access-logfile"),
    default=conf.get('webserver', 'ACCESS_LOGFILE'),
    help="The logfile to store the webserver access log. Use '-' to print to stderr",
)
ARG_ERROR_LOGFILE = Arg(
    ("-E", "--error-logfile"),
    default=conf.get('webserver', 'ERROR_LOGFILE'),
    help="The logfile to store the webserver error log. Use '-' to print to stderr",
)
ARG_ACCESS_LOGFORMAT = Arg(
    ("-L", "--access-logformat"),
    default=conf.get('webserver', 'ACCESS_LOGFORMAT'),
    help="The access log format for gunicorn logs",
)
ARG_NUM_RUNS = Arg(
    ("-n", "--num-runs"),
    default=conf.getint('scheduler', 'num_runs'),
    type=int,
    help="Set the number of runs to execute before exiting",
)
ARG_DO_PICKLE = Arg(
    ("-p", "--do-pickle"),
    default=False,
    help=(
        "Attempt to pickle the DAG object to send over "
        "to the workers, instead of letting workers run their version "
        "of the code"
    ),
    action="store_true",