Add long-context document analysis use-case example for Nemotron 3 Super

Problem:
Nemotron 3 Super's 1M native context window is one of its strongest differentiators - outperforming GPT-OSS and Qwen3.5 on RULER at 1M context, and offering 8x the context length of Qwen 3.5 (128K). The README highlights this capability prominently, but the repository has no use-case example demonstrating practical long-context document analysis.

The existing examples use short prompts (chat, math, basic generation, tool calling) and don't showcase the ability to process large document collections in a single context window - which is exactly the use case that makes 1M context transformative for enterprise users.

Proposed Solution:
Add a Jupyter notebook use-case example in use-case-examples/Long-Context-Document-Analysis-with-Nemotron-Super/ that demonstrates:

1. Building and loading a large document corpus (100K-500K tokens) into a single context window
2. Single-document and multi-document analysis without chunking or RAG
3. Cross-document theme extraction and synthesis
4. Context length scaling comparison (32K vs 128K vs 256K vs 512K)
5. Best practices for prompt formatting with large contexts

The example uses a self-contained synthetic corpus (no external data dependencies) and follows the existing use-case-examples/ pattern with README + notebook.

Why now:
With Nemotron 3 Super just launched and GTC next week, community interest in the 1M context capability is at its peak. r/LocalLLaMA threads specifically cite the context window as the deciding factor vs competing models.

I'm willing to implement this. Happy to adjust based on feedback.