## Problem Description and Formulation

The problem is an optimization problem with two variables: 'milligrams of calcium' and 'milligrams of vitamin B7'. The objective is to minimize the function 5.78 times the number of milligrams of calcium added to 5.62 times the number of milligrams of vitamin B7.

The problem has several constraints:
- The kidney support index for milligrams of calcium is 13.
- Milligrams of calcium have a cognitive performance index of 1.
- Milligrams of calcium have a muscle growth index of 13.
- The kidney support index for milligrams of vitamin B7 is 11.
- The cognitive performance index of milligrams of vitamin B7 is 1.
- Milligrams of vitamin B7 have a muscle growth index of 11.
- The total combined kidney support index from milligrams of calcium and milligrams of vitamin B7 should be at least 44.
- The total combined cognitive performance index from milligrams of calcium and milligrams of vitamin B7 should be at least 15.
- The total combined muscle growth index from milligrams of calcium and milligrams of vitamin B7 should be at least 13.
- 4 times the number of milligrams of calcium plus -9 times the number of milligrams of vitamin B7 should be at least zero.
- The total combined kidney support index from milligrams of calcium and milligrams of vitamin B7 should be at most 98.
- The total combined cognitive performance index from milligrams of calcium and milligrams of vitamin B7 should be at most 53.
- The total combined muscle growth index from milligrams of calcium and milligrams of vitamin B7 should be at most 34.
- The number of milligrams of calcium must be a whole number.
- The number of milligrams of vitamin B7 can be a non-integer.

## Gurobi Code Formulation

```python
import gurobi

def optimize_nutrition():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define variables
    calcium = model.addVar(name="calcium", vtype=gurobi.GRB.INTEGER)  # Milligrams of calcium
    vitamin_b7 = model.addVar(name="vitamin_b7")  # Milligrams of vitamin B7

    # Objective function: Minimize 5.78*calcium + 5.62*vitamin_b7
    model.setObjective(5.78 * calcium + 5.62 * vitamin_b7, gurobi.GRB.MINIMIZE)

    # Constraints
    # Individual indexes
    model.addConstr(13 * calcium <= 112, name="calcium_kidney_support")
    model.addConstr(1 * calcium <= 80, name="calcium_cognitive_performance")
    model.addConstr(13 * calcium <= 50, name="calcium_muscle_growth")

    model.addConstr(11 * vitamin_b7 <= 112, name="vitamin_b7_kidney_support")
    model.addConstr(1 * vitamin_b7 <= 80, name="vitamin_b7_cognitive_performance")
    model.addConstr(11 * vitamin_b7 <= 50, name="vitamin_b7_muscle_growth")

    # Combined indexes
    model.addConstr(13 * calcium + 11 * vitamin_b7 >= 44, name="combined_kidney_support_min")
    model.addConstr(13 * calcium + 11 * vitamin_b7 <= 98, name="combined_kidney_support_max")

    model.addConstr(1 * calcium + 1 * vitamin_b7 >= 15, name="combined_cognitive_performance_min")
    model.addConstr(1 * calcium + 1 * vitamin_b7 <= 53, name="combined_cognitive_performance_max")

    model.addConstr(13 * calcium + 11 * vitamin_b7 >= 13, name="combined_muscle_growth_min")
    model.addConstr(13 * calcium + 11 * vitamin_b7 <= 34, name="combined_muscle_growth_max")

    # Additional constraint
    model.addConstr(4 * calcium - 9 * vitamin_b7 >= 0, name="additional_constraint")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Milligrams of Calcium: {calcium.varValue}")
        print(f"Milligrams of Vitamin B7: {vitamin_b7.varValue}")
        print(f"Objective Function Value: {model.objVal}")
    else:
        print("The model is infeasible.")

optimize_nutrition()
```