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Recent advances in pathology foundation models, pre-trained on large-scale histopathology images, have greatly advanced disease-focused applications. At the same time, spatial multi-omic technologies now measure gene and protein expression with high spatial resolution, offering valuable insights into tissue context. Yet, existing models struggle to integrate these complementary data types. Here, to address this challenge, we present spEMO, a computational framework that unifies embeddings from pathology foundation models and large language models for spatial multi-omic analysis. By leveraging multi-modal representations, spEMO surpasses single-modality models across diverse downstream tasks, including spatial domain identification, spot-type classification, whole-slide disease prediction and interpretation, multicellular interaction inference and automated medical reporting. These results highlight spEMO's strength in both biological discovery and clinical applications. Furthermore, we introduce a new benchmark task-multi-modal alignment-to evaluate how effectively pathology foundation models retrieve complementary information. Together, spEMO establishes a powerful step towards holistic, interpretable and generalizable AI for spatial biology and pathology.