Explore returning json response instead of pydantic models for better serialization performance

### Description

While looking into fastapi discussions I noticed that returning models could result in revalidation of the model though the validation was done during model construction. There were also issues with serialization using jsonable_encoder mentioned in the thread. To validate this I did some combinations benchmark to return pydantic model, serialized response using json and serialized response using orjson after validation for a simple "ui/dags" endpoint. Returning json or orjson encoded response seems to result in 2x requests per second.

Tradeoffs

* Using orjson results in extra dependency and in that case FastAPI JSONResponse can be used as builtin option.
* Skipping validation for response could be
* Need to check how automatic API generation works when return value is not specified in method.

Start below as app.py with 10 workers in uvicorn using `uvicorn app:app --workers 10 --port 8000` command. Start a benchmark session for the endpoints with fixed response.

Benchmark for different endpoints

1. Return pydantic model with no return value signature
2. Return orjson response with pydantic model in return value signature but no response validation
3. Return orjson response with no model in return value signature but response validated using pydantic
4. Response dict validated with pydantic and dict returned with model in return value signature.
5. Return json response with model validation for response but no model in return signature

https://github.com/fastapi/fastapi/issues/1359
https://github.com/fastapi/fastapi/issues/1224#issuecomment-617243856

```
seq 1 5 | xargs -I{} time hey -c 100 -n 10000 http://localhost:8000/dags\{\} | grep -iE 'Average:|Requests/sec'
Average: 0.0137 secs
Requests/sec: 5766.3920
1.05user 0.47system 0:01.75elapsed 86%CPU (0avgtext+0avgdata 20088maxresident)k
0inputs+0outputs (1major+4194minor)pagefaults 0swaps
Average: 0.0059 secs
Requests/sec: 15063.7350
0.86user 0.34system 0:00.68elapsed 178%CPU (0avgtext+0avgdata 21236maxresident)k
0inputs+0outputs (0major+4595minor)pagefaults 0swaps
Average: 0.0063 secs
Requests/sec: 13714.7256
0.89user 0.35system 0:00.74elapsed 167%CPU (0avgtext+0avgdata 26076maxresident)k
0inputs+0outputs (0major+5741minor)pagefaults 0swaps
Average: 0.0134 secs
Requests/sec: 7052.8467
1.03user 0.41system 0:01.43elapsed 100%CPU (0avgtext+0avgdata 19932maxresident)k
0inputs+0outputs (0major+4202minor)pagefaults 0swaps
Average: 0.0070 secs
Requests/sec: 13043.1176
0.87user 0.38system 0:00.78elapsed 160%CPU (0avgtext+0avgdata 23416maxresident)k
0inputs+0outputs (0major+5021minor)pagefaults 0swaps
```

app.py

```python
from __future__ import annotations

import orjson
from fastapi import FastAPI
from fastapi.responses import JSONResponse

from airflow.api_fastapi.core_api.datamodels.monitor import HealthInfoResponse
from airflow.api_fastapi.core_api.datamodels.ui.dags import (
DAGWithLatestDagRunsCollectionResponse,
)

app = FastAPI()

class ORJSONCustomResponse(JSONResponse):
media_type = "application/json"

def render(self, content) -> bytes:
return orjson.dumps(content, option=orjson.OPT_NON_STR_KEYS | orjson.OPT_UTC_Z)

@app.get("/health1")
def get_health() -> HealthInfoResponse:
res = {
"metadatabase": {"status": "healthy"},
"scheduler": {
"status": "unhealthy",
"latest_scheduler_heartbeat": "2025-12-04T15:14:35.876302+00:00",
},
"triggerer": {"status": None, "latest_triggerer_heartbeat": None},
"dag_processor": {
"status": "unhealthy",
"latest_dag_processor_heartbeat": "2025-12-04T16:04:49.023929+00:00",
},
}
return HealthInfoResponse.model_validate(res)

@app.get("/health2", response_model=HealthInfoResponse, response_class=ORJSONCustomResponse)
def get_health2() -> HealthInfoResponse:
res = {
"metadatabase": {"status": "healthy"},
"scheduler": {
"status": "unhealthy",
"latest_scheduler_heartbeat": "2025-12-04T15:14:35.876302+00:00",
},
"triggerer": {"status": None, "latest_triggerer_heartbeat": None},
"dag_processor": {
"status": "unhealthy",
"latest_dag_processor_heartbeat": "2025-12-04T16:04:49.023929+00:00",
},
}
return ORJSONCustomResponse(res)

@app.get("/health3")
def get_health3():
res = {
"metadatabase": {"status": "healthy"},
"scheduler": {
"status": "unhealthy",
"latest_scheduler_heartbeat": "2025-12-04T15:14:35.876302+00:00",
},
"triggerer": {"status": None, "latest_triggerer_heartbeat": None},
"dag_processor": {
"status": "unhealthy",
"latest_dag_processor_heartbeat": "2025-12-04T16:04:49.023929+00:00",
},
}
return res

res = {
"total_entries": 2,
"dags": [
{
"dag_id": "gh58560",
"dag_display_name": "gh58560",
"is_paused": False,
"is_stale": False,
"last_parsed_time": "2025-12-04T16:04:24.620735Z",
"last_parse_duration": 0.06622079999942798,
"bundle_name": "dags-folder-1",
"relative_fileloc": "gh58560.py",
"fileloc": "/home/karthikeyan/airflow/dagsgh58560/gh58560.py",
"timetable_description": "Never, external triggers only",
"tags": [],
"description": None,
"next_dagrun": None,
"next_dagrun_data_interval_start": None,
"next_dagrun_data_interval_end": None,
"next_dagrun_create_after": None,
"last_expired": None,
"timetable_summary": None,
"asset_expression": None,
"bundle_version": None,
"max_active_tasks": 16,
"max_active_runs": 16,
"max_consecutive_failed_dag_runs": 0,
"has_task_concurrency_limits": False,
"has_import_errors": False,
"owners": ["airflow"],
"latest_dag_runs": [
{
"id": 1,
"dag_id": "gh58560",
"run_id": "manual__2025-12-04T14:57:33+00:00",
"logical_date": "2025-12-04T14:57:33Z",
"run_after": "2025-12-04T14:57:33Z",
"start_date": "2025-12-04T15:19:37.873413Z",
"end_date": "2025-12-04T15:19:37.873413Z",
"state": "success",
}
],
"pending_actions": [],
"is_favorite": False,
"file_token": "eyJidW5kbGVfbmFtZSI6ImRhZ3MtZm9sZGVyLTEiLCJyZWxhdGl2ZV9maWxlbG9jIjoiZ2g1ODU2MC5weSJ9.bLgsjNGrIc5g1XjK1mzyqIOLPWM",
},
{
"dag_id": "gh58560_1",
"dag_display_name": "gh58560_1",
"is_paused": False,
"is_stale": False,
"last_parsed_time": "2025-12-04T16:04:24.695772Z",
"last_parse_duration": 0.09828795900102705,
"bundle_name": "dags-folder-1",
"relative_fileloc": "gh58560_1.py",
"fileloc": "/home/karthikeyan/airflow/dagsgh58560/gh58560_1.py",
"timetable_description": "Never, external triggers only",
"tags": [],
"description": None,
"next_dagrun": None,
"next_dagrun_data_interval_start": None,
"next_dagrun_data_interval_end": None,
"next_dagrun_create_after": None,
"last_expired": None,
"timetable_summary": None,
"asset_expression": None,
"bundle_version": None,
"max_active_tasks": 16,
"max_active_runs": 16,
"max_consecutive_failed_dag_runs": 0,
"has_task_concurrency_limits": False,
"has_import_errors": False,
"owners": ["airflow"],
"latest_dag_runs": [],
"pending_actions": [],
"is_favorite": False,
"file_token": "eyJidW5kbGVfbmFtZSI6ImRhZ3MtZm9sZGVyLTEiLCJyZWxhdGl2ZV9maWxlbG9jIjoiZ2g1ODU2MF8xLnB5In0.-xfPS1613MuegDAlA93RyV_K1kI",
},
],
}

@app.get("/dags1")
def get_dags1():
return DAGWithLatestDagRunsCollectionResponse(total_entries=res["total_entries"], dags=res["dags"])

@app.get("/dags2")
def get_dags2() -> DAGWithLatestDagRunsCollectionResponse:
return ORJSONCustomResponse(res)

@app.get("/dags3")
def get_dags3():
DAGWithLatestDagRunsCollectionResponse(total_entries=res["total_entries"], dags=res["dags"])
return ORJSONCustomResponse(res)

@app.get("/dags4")
def get_dags4() -> DAGWithLatestDagRunsCollectionResponse:
DAGWithLatestDagRunsCollectionResponse(total_entries=res["total_entries"], dags=res["dags"])
return res

@app.get("/dags5")
def get_dags5():
DAGWithLatestDagRunsCollectionResponse(total_entries=res["total_entries"], dags=res["dags"])
return JSONResponse(res)

```

Edit : Microbenchmarks could be misleading and please add in if I measured something wrong.

### Use case/motivation

_No response_

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] 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)

Start with the provided app.py benchmark and compare the /health1-/health3 and /dags1-/dags5 response paths, including DAGWithLatestDagRunsCollectionResponse. Verify the response-validation and automatic API-generation behavior, then establish benchmarks and tests for any agreed serialization approach.

Written by the indexing model from the issue text.