## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin D' and 'milligrams of iron'. Let's denote 'milligrams of vitamin D' as $x_1$ and 'milligrams of iron' as $x_2$. The objective function to maximize is $1 \cdot x_1 + 8 \cdot x_2$. The constraints are:
- The kidney support index for $x_1$ is 17, and for $x_2$ is 1.
- The total combined kidney support index must be as much or more than 48: $17x_1 + x_2 \geq 48$.
- $-9x_1 + x_2 \geq 0$.
- The total combined kidney support index must be 74 or less: $17x_1 + x_2 \leq 74$.

## 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin D'
- $x_2$ for 'milligrams of iron'

The objective function is: $1x_1 + 8x_2$

The constraints are:
- $17x_1 + x_2 \geq 48$
- $-9x_1 + x_2 \geq 0$
- $17x_1 + x_2 \leq 74$

## 3: Express the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin D'), ('x2', 'milligrams of iron')],
    'objective_function': '1*x1 + 8*x2',
    'constraints': [
        '17*x1 + x2 >= 48',
        '-9*x1 + x2 >= 0',
        '17*x1 + x2 <= 74'
    ]
}
```

## 4: Implement the problem using Gurobi
To solve this problem using Gurobi, we will use Python. We need to import the Gurobi library, define the model, add the variables, the objective function, and the constraints.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin D
    x2 = model.addVar(name="x2", lb=0)  # milligrams of iron

    # Define the objective function
    model.setObjective(x1 + 8 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(17 * x1 + x2 >= 48)
    model.addConstr(-9 * x1 + x2 >= 0)
    model.addConstr(17 * x1 + x2 <= 74)

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin D: {x1.varValue}")
        print(f"Milligrams of iron: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```