### Description

When using a ML model with that outputs sparse vectors (like ELSER), it's possible that some times the inference pipeline fails when indexing new documents and new ingested documents are not enriched with the `sparse_vector` fields.

One approach in this case is to run an update by query and issue a reindexing with the ml pipeline of the documents in place in the same index.
In this case, we would want to only update the documents that do not have `sparse_vector` fields, for example:

```
POST search-books/_update_by_query?pipeline=ml-inference-books
{
  "query": {
    "bool": {
      "must_not": {
        "exists": {
          "field": "ml.inference.description_expanded.predicted_value"
        }
      }
    }
  }
}
```
In theory this a much simpler approach then duplicating all the data to reindex in another index or to reissue bulk indexing requests from scratch. It only requires a single Elasticsearch API request.

the problem is that `sparse_vector` fields do not support `exists` queries.

```
{
  "error": {
    "root_cause": [
      {
        "type": "query_shard_exception",
        "reason": "failed to create query: [sparse_vector] fields do not support [exists] queries",
        "index_uuid": "WkxxefxdTuCiNeLy4b33jw",
        "index": "search-books"
      }
    ],
    "type": "search_phase_execution_exception",
    "reason": "all shards failed",
    "phase": "query",
    "grouped": true,
    "failed_shards": [
      {
        "shard": 0,
        "index": "search-books",
        "node": "l5lxDFkeSwOyiwOEkQtWZA",
        "reason": {
          "type": "query_shard_exception",
          "reason": "failed to create query: [sparse_vector] fields do not support [exists] queries",
          "index_uuid": "WkxxefxdTuCiNeLy4b33jw",
          "index": "search-books",
          "caused_by": {
            "type": "illegal_argument_exception",
            "reason": "[sparse_vector] fields do not support [exists] queries"
          }
        }
      }
    ]
  },
  "status": 400
}
```

This also makes it difficult to get an accurate count of how many fields are missing the `sparse_vector` fields and users have to rely on other fields/mechanisms to do a reindexing in place with update by query.

NOTE: This will only be for the top level sparse_vector field. NOT for any of its sparse dimensions.

server/src/main/java/org/elasticsearch/index/IndexVersion.java
* Returns the minimum version of {@code version1} and {@code version2}
     */
    public static IndexVersion min(IndexVersion version1, IndexVersion version2) {
        return version1.id < version2.id ? version1 : version2;
    }
    /**
     * Returns the maximum version of {@code version1} and {@code version2}
     */
    public static IndexVersion max(IndexVersion version1, IndexVersion version2) {
        return version1.id > version2.id ? version1 : version2;
    }
    /**
     * Returns the most recent index version.
     * This should be the index version with the highest id.
     */
    public static IndexVersion current() {
        return CurrentHolder.CURRENT;
    }
    public boolean isLegacyIndexVersion() {
        return before(MINIMUM_COMPATIBLE);
    }
    public static IndexVersion getMinimumCompatibleIndexVersion(int versionId) {
        int major = versionId / 1_000_000;
        if (major == IndexVersion.current().id() / 1_000_000) {
            // 
            return IndexVersion.MINIMUM_COMPATIBLE;
        } else {
            int compatId = (major-1) * 1_000_000;
            if (major <= 8) compatId += 99;
            return IndexVersion.fromId(compatId);