[Misc][V0 Deprecation] Remove Redundant Offline Distributed Inference Example (#1899)

### What this PR does / why we need it?
The file `offline_distributed_inference_npu.py` is the same as
`offline_inference_npu_tp2.py`, thus we delete one of them.

This PR is a part of
https://github.com/vllm-project/vllm-ascend/issues/1620.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.9.2
- vLLM main:
8188196a1c

Signed-off-by: shen-shanshan <467638484@qq.com>

examples/offline_distributed_inference_npu.py

@@ -1,53 +0,0 @@
-#
-# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
-# Copyright 2023 The vLLM team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# This file is a part of the vllm-ascend project.
-# Adapted from vllm-project/vllm/examples/offline_inference/basic.py
-#
-
-import os
-from vllm import LLM, SamplingParams
-
-os.environ["VLLM_USE_MODELSCOPE"] = "True"
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
-def main():
-    prompts = [
-        "Hello, my name is",
-        "The president of the United States is",
-        "The capital of France is",
-        "The future of AI is",
-    ]
-
-    # Create a sampling params object.
-    sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
-    # Create an LLM.
-    llm = LLM(
-        model="Qwen/Qwen2.5-0.5B-Instruct",
-        tensor_parallel_size=2,
-        distributed_executor_backend="mp",
-        trust_remote_code=True,
-    )
-
-    # Generate texts from the prompts.
-    outputs = llm.generate(prompts, sampling_params)
-    for output in outputs:
-        prompt = output.prompt
-        generated_text = output.outputs[0].text
-        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-
-
-if __name__ == "__main__":
-    main()