## Problem Description and Formulation

The problem requires maximizing the objective function: 

`1 * milligrams of vitamin D + 8 * milligrams of iron`

subject to several constraints:

1. The kidney support index for milligrams of vitamin D is 17.
2. Milligrams of iron each have a kidney support index of 1.
3. The total combined kidney support index must be greater than or equal to 48.
4. `-9 * milligrams of vitamin D + 1 * milligrams of iron >= 0`.
5. The total combined kidney support index must be less than or equal to 74.

## Gurobi Code Formulation

The problem can be formulated and solved using Gurobi, a powerful optimization solver. Below is the Python code that captures the problem description and provides a solution.

```python
import gurobi

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

    # Define variables
    vitamin_d = model.addVar(name="vitamin_d", lb=0)  # milligrams of vitamin D
    iron = model.addVar(name="iron", lb=0)  # milligrams of iron

    # Objective function: maximize 1 * vitamin_d + 8 * iron
    model.setObjective(vitamin_d + 8 * iron, gurobi.GRB.MAXIMIZE)

    # Constraints
    # Kidney support index for vitamin D is 17
    # Kidney support index for iron is 1
    # Total kidney support index >= 48
    model.addConstraint(17 * vitamin_d + iron >= 48)

    # -9 * vitamin_d + iron >= 0
    model.addConstraint(-9 * vitamin_d + iron >= 0)

    # Total kidney support index <= 74
    model.addConstraint(17 * vitamin_d + iron <= 74)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin D: {vitamin_d.varValue}")
        print(f"Milligrams of iron: {iron.varValue}")
        print(f"Objective: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

optimize_vitamins_and_iron()
```