### 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/RunDataFrameAnalyticsIT.java
);
    }
    public void testStopOutlierDetectionWithEnoughDocumentsToScroll() throws Exception {
        String sourceIndex = "test-stop-outlier-detection-with-enough-docs-to-scroll";
        client().admin()
            .indices()
            .prepareCreate(sourceIndex)
            .setMapping("numeric_1", "type=double", "numeric_2", "type=float", "categorical_1", "type=keyword")
            .get();
        BulkRequestBuilder bulkRequestBuilder = client().prepareBulk();
        bulkRequestBuilder.setRefreshPolicy(WriteRequest.RefreshPolicy.IMMEDIATE);
        int docCount = randomIntBetween(1024, 2048);
        for (int i = 0; i < docCount; i++) {
            IndexRequest indexRequest = new IndexRequest(sourceIndex);
            indexRequest.source("numeric_1", randomDouble(), "numeric_2", randomFloat(), "categorical_1", randomAlphaOfLength(10));
            bulkRequestBuilder.add(indexRequest);
        }
        BulkResponse bulkResponse = bulkRequestBuilder.get();
        if (bulkResponse.hasFailures()) {
            fail("Failed to index data: " + bulkResponse.buildFailureMessage());
        }
        String id = "test_stop_outlier_detection_with_enough_docs_to_scroll";
        DataFrameAnalyticsConfig config = buildAnalytics(
            id,
            sourceIndex,
            sourceIndex + "-results",
            "custom_ml",
            new OutlierDetection.Builder().build()
        );
        putAnalytics(config);