Our research addresses the challenge of answering complex scholarly hybrid questions, often demanding multi-faceted reasoning and iterative answer retrieval over scholarly knowledge graphs (KGs) and text. The question complexity is simplified by decomposing it into simple questions and utilizing symbolic representation. However, existing scholarly hybrid Question Answering (QA) models lack question decomposition and symbolic representation. In response, we propose SH-CoDE (Scholarly Hybrid Complex Question !!ecomposition and Execution). This approach breaks down questions into simple queries and employs symbolic representations, resulting in a natural and interpretable format - HQ (Hybrid Question) representation. SH-CoDE also includes an HQ-Executor, transforming the HQ representation into a tree structure and executing operations within its nodes. During execution, if the executor encounters symbolic representations such as KGQA or TextQA, it retrieves answers from KG and text data sources, respectively. The KGQA module automatically generates and runs SPARQL queries against the KG SPARQL endpoints. Similarly, the TextQA component employs semantic searching and an FLAN - T5-based reader to answer over text. Our model demonstrates competitive results on the test dataset, showcasing its effectiveness in answering complex scholarly Questions.