### 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/test/java/org/elasticsearch/index/mapper/vectors/SparseVectorFieldMapperTests.java
float[] indexedValues = { 0.5f, 1800f, -34567.11f };
        BytesReference source = BytesReference.bytes(
            XContentFactory.jsonBuilder()
                .startObject()
                .startObject("my-vector")
                .field(Integer.toString(indexedDims[0]), indexedValues[0])
                .field(Integer.toString(indexedDims[1]), indexedValues[1])
                .field(Integer.toString(indexedDims[2]), indexedValues[2])
                .endObject()
                .endObject()
        );
        DocumentParsingException indexException = expectThrows(
            DocumentParsingException.class,
            () -> mapper.parse(new SourceToParse("id", source, XContentType.JSON))
        );
        assertThat(indexException.getCause().getMessage(), containsString(SparseVectorFieldMapper.ERROR_MESSAGE_7X));
    }
    public void testSparseVectorUnsupportedIndex() throws Exception {
        IndexVersion version = IndexVersionUtils.randomVersionBetween(
            random(),
            PREVIOUS_SPARSE_VECTOR_INDEX_VERSION,
            IndexVersion.V_8_500_000
        );
        Exception e = expectThrows(MapperParsingException.class, () -> createMapperService(version, fieldMapping(b -> {
            b.field("type", "sparse_vector");
        })));
        assertThat(e.getMessage(), containsString(SparseVectorFieldMapper.ERROR_MESSAGE_8X));
    }
}