In long structured document retrieval, existing methods typically fine-tune pre-trained language models (PLMs) using contrastive learning on datasets lacking explicit structural information.This practice suffers from two critical issues: 1) current methods fail to leverage structural features and element-level semantics effectively, and 2) the lack of datasets containing structural metadata.To bridge these gaps, we propose SEAL, a novel contrastive learning framework.It leverages structure-aware learning to preserve semantic hierarchies and masked element alignment for fine-grained semantic discrimination.Furthermore, we release StructDocRetrieval, a long structured document retrieval dataset with rich structural annotations.Extensive experiments on both released and industrial datasets across various modern PLMs, along with online A/B testing, demonstrate consistent performance improvements, boosting NDCG@10 from 79.41% to 82.59% on BGE-M3.The resources are available at this URL.