fix: add support for custom mapping in ElasticsearchDocumentStore

[AnushreeBannadabhavi]

Fixes: https://github.com/deepset-ai/haystack-core-integrations/issues/655

Added support for custom mapping when creating a new index using ElasticsearchDocumentStore.

Tested the fix using the below code:

from haystack_integrations.document_stores.elasticsearch import ElasticsearchDocumentStore
from haystack import Pipeline
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.converters import TextFileToDocument
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter 
from haystack_integrations.components.retrievers.elasticsearch import ElasticsearchEmbeddingRetriever

mappings = {
    "properties": {
        "embedding": {"type": "dense_vector", "index": True, "similarity": "dot_product"},
        "content": {"type": "text"},
    },
    "dynamic_templates": [
        {
            "strings": {
                "path_match": "*",
                "match_mapping_type": "string",
                "mapping": {
                    "type": "keyword",
                },
            }
        }
    ],
}

document_store = ElasticsearchDocumentStore(hosts = "http://localhost:9200", custom_mapping=mappings)
converter = TextFileToDocument()
splitter = DocumentSplitter()
doc_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
writer = DocumentWriter(document_store)

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("converter", converter)
indexing_pipeline.add_component("splitter", splitter)
indexing_pipeline.add_component("doc_embedder", doc_embedder)
indexing_pipeline.add_component("writer", writer)

indexing_pipeline.connect("converter", "splitter")
indexing_pipeline.connect("splitter", "doc_embedder")
indexing_pipeline.connect("doc_embedder", "writer")

indexing_pipeline.run({
    "converter":{"sources":["filename.txt"]}
    })