In e-commerce, search and recommendation rankings require a deep understanding of user behaviors and personalized scoring of products. While existing systems maintain separate pipelines for search and recommendation, these two scenarios share aligned objectives and exhibit consistent data patterns during ranking. To address this, we propose a joint modeling approach for search-recommendation ranking that enables information gain exchange between the two scenarios, thus facilitating enhanced modeling of users' cross-scenario behaviors. Our proposed SuperRS framework employs a Dual-layer Multi-MoE (DualMoE) architecture to tackle scenario-specific disparities and achieve multi-interest fusion perception. A key aspect is the Search-Recommendation Sequence Fusion Unit, which integrates user interaction sequences from both scenarios. Additionally, we introduce a unified Representation Extraction method utilizing Reciprocal Scenario Interest Attention (RSIA) for feature alignment. Dynamic Feature Integration (DFI) employs a dual gating mechanism for controlled information fusion while preserving scenario identities, combined with multi-objective optimization. On the 1688 App, our framework demonstrates superior performance to baseline models across both offline evaluation metrics and online business indicators.