Graph learning for urban region modeling has gained significant attention for leveraging multi-modal data to generate region representations for downstream task prediction. However, existing models face two key limitations: (1) they primarily adopt a global perspective, overlooking the joint modeling of both local and global aspects, and (2) they rely on redundant, low-information nodes, leading to suboptimal region representations. To address these challenges, we propose GraphJCL, a dual-perspective framework that models both local and global perspectives. Specifically, GraphJCL first constructs local graphs for individual regions and a global graph encompassing all regions, integrating POI, taxi flow, remote sensing, street view, and road network data. Additionally, GraphJCL employs specialized message-passing mechanisms to efficiently capture both local and global graph node representations. Furthermore, GraphJCL incorporates entropy-optimized graph node pruning, retaining only the most informative nodes to enhance final region representations. To ensure the effectiveness of the designed dual-perspective graph framework, GraphJCL introduces a joint contrastive learning approach, optimizing region representations through geography-driven, entropy-optimized, and mutual information-based optimization techniques. Extensive experiments on two real-world datasets across five modalities demonstrate that GraphJCL consistently outperforms state-of-the-art methods on three tasks, validating its flexibility and effectiveness.