### Description

This applies to the following APIs:
- [x] open anomaly detection
- [x] start datafeed
- [x] start data frame analytics job
- [x] start trained model deployment

Currently, if the operation takes longer than the configured `timeout`, the response status code is `500`.

With Kibana in mind, a `4xx` response would be more suitable as it will lead to showing more useful information to the user about why the request failed.

Looking at [RFC 9110](https://www.rfc-editor.org/rfc/rfc9110.html#name-408-request-timeout), it seems the most appropriate `4xx` code would be `408 Request Timeout`, even though the RFC describes a different situation. 

The alternative, `504 Gateway Timeout`, would be more appropriate for this situation but it wouldn't improve the user experience in Kibana.
x-pack/plugin/ml/qa/native-multi-node-tests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/MlJobIT.java
String indicesAfterDelete = EntityUtils.toString(
            client().performRequest(new Request("GET", "/_cat/indices/" + AnomalyDetectorsIndexFields.RESULTS_INDEX_PREFIX + "*"))
                .getEntity()
        );
        assertThat(indicesAfterDelete, containsString(indexName));
        waitUntilIndexIsEmpty(indexName);
        
        expectThrows(
            ResponseException.class,
            () -> client().performRequest(new Request("GET", MachineLearning.BASE_PATH + "anomaly_detectors/" + jobId + "/_stats"))
        );
    }
    public void testOutOfOrderData() throws Exception {
        String jobId = "job-with-out-of-order-docs";
        createFarequoteJob(jobId);
        openJob(jobId);
        Request postDataRequest = new Request("POST", MachineLearning.BASE_PATH + "anomaly_detectors/" + jobId + "/_data");
        
        postDataRequest.setOptions(POST_DATA);
        
        postDataRequest.setJsonEntity("{ \"airline\":\"LOT\", \"responsetime\":100, \"time\":\"2019-07-01 00:00:00Z\" }");
        client().performRequest(postDataRequest);
        postDataRequest.setJsonEntity("{ \"airline\":\"LOT\", \"responsetime\":100, \"time\":\"2019-07-01 00:30:00Z\" }");
        client().performRequest(postDataRequest);
        
        postDataRequest.setJsonEntity("{ \"airline\":\"LOT\", \"responsetime\":100, \"time\":\"2019-07-01 00:10:00Z\" }");
        client().performRequest(postDataRequest);
        Response flushResponse = client().performRequest(
            new Request("POST", MachineLearning.BASE_PATH + "anomaly_detectors/" + jobId + "/_flush")
        );