Hierarchical Document Structure Analysis (HDSA) aims to recover the logical tree and reading order of a document, providing a structural foundation for downstream tasks such as document question answering, information extraction, and long-form document understanding. Extending HDSA to multi-page documents remains challenging, as pagination introduces discontinuities between page-local coordinate systems, making it difficult to link structurally related elements across pages. We present Scroll-Net, a geometry-aware constrained decoding framework for multi-page document hierarchy parsing. Scroll-Net maps page-local coordinates into a unified scroll-space coordinate frame, enabling direct comparison of cross-page elements. It employs a dual-stream encoder to disentangle geometric and semantic cues, injecting geometric inductive bias before multimodal fusion. Under the pre-order convention used by the evaluated benchmarks, Scroll-Net constrains autoregressive parent decoding with a hard reading-order causal mask. This backward-pointer constraint makes decoded parent graphs acyclic under the imposed order while retaining O(K) per-step decoding, reducing inference latency by 36.5% and peak memory by 46.5%. Across four benchmarks, Scroll-Net remains competitive on simple layouts while achieving state-of-the-art results on structurally complex documents, improving TEDS by 4.8 points on DocHieNet.