comminity[patch]: fix #25575 YandexGPTs for _grpc_metadata (#25617)

it fixes two issues:

### YGPTs are broken #25575

```
File ....conda/lib/python3.11/site-packages/langchain_community/embeddings/yandex.py:211, in _make_request(self, texts, **kwargs)
..
--> 211 res = stub.TextEmbedding(request, metadata=self._grpc_metadata)  # type: ignore[attr-defined]

AttributeError: 'YandexGPTEmbeddings' object has no attribute '_grpc_metadata'
```
My gut feeling that #23841 is the cause.

I have to drop leading underscore from `_grpc_metadata` for quickfix,
but I just don't know how to do it _pydantic_ enough.

### minor issue:

if we use `api_key`, which is not the best practice the code fails with 

```
File ~/git/...../python3.11/site-packages/langchain_community/embeddings/yandex.py:119, in YandexGPTEmbeddings.validate_environment(cls, values)
...

AttributeError: 'tuple' object has no attribute 'append'
```

- Added new integration test. But it requires YGPT env available and
active account. I don't know how int tests dis\enabled in CI.
 - added small unit tests with mocks. Should be fine.

---------

Co-authored-by: mikhail-khludnev <mikhail_khludnev@rntgroup.com>

libs/community/tests/integration_tests/embeddings/test_yandex.py

@@ -0,0 +1,31 @@
+import pytest
+
+from langchain_community.embeddings.yandex import YandexGPTEmbeddings
+
+
+@pytest.mark.parametrize(
+    "constructor_args",
+    [
+        dict(),
+        dict(disable_request_logging=True),
+    ],
+)
+# @pytest.mark.scheduled - idk what it means
+# requires YC_* env and active service
+def test_yandex_embedding(constructor_args: dict) -> None:
+    documents = ["exactly same", "exactly same", "different"]
+    embedding = YandexGPTEmbeddings(**constructor_args)
+    doc_outputs = embedding.embed_documents(documents)
+    assert len(doc_outputs) == 3
+    for i in range(3):
+        assert len(doc_outputs[i]) >= 256  # there are many dims
+        assert len(doc_outputs[0]) == len(doc_outputs[i])  # dims are te same
+    assert doc_outputs[0] == doc_outputs[1]  # same input, same embeddings
+    assert doc_outputs[2] != doc_outputs[1]  # different input, different embeddings
+
+    qry_output = embedding.embed_query(documents[0])
+    assert len(qry_output) >= 256
+    assert len(doc_outputs[0]) == len(
+        qry_output
+    )  # query and doc models have same dimensions
+    assert doc_outputs[0] != qry_output  # query and doc models are different