## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin K' and 'milligrams of vitamin A', which we can denote as $x_0$ and $x_1$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to maximize is $9x_0 + 2x_1$.

## Step 3: List the constraints
The constraints given are:
- $2x_0 + 15x_1 \geq 62$ (cardiovascular support index minimum)
- $7x_0 + 11x_1 \geq 74$ (kidney support index minimum)
- $5x_0 + 8x_1 \geq 11$ (immune support index minimum)
- $2x_0 + 8x_1 \geq 34$ (cognitive performance index minimum)
- $-4x_0 + 5x_1 \geq 0$
- $2x_0 + 15x_1 \leq 82$ (cardiovascular support index maximum)
- $7x_0 + 11x_1 \leq 111$ (kidney support index maximum)
- $5x_0 + 8x_1 \leq 49$ (immune support index maximum)
- $2x_0 + 8x_1 \leq 98$ (cognitive performance index maximum)

## 4: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x0', 'milligrams of vitamin K'), ('x1', 'milligrams of vitamin A')],
'objective_function': '9*x0 + 2*x1',
'constraints': [
    '2*x0 + 15*x1 >= 62',
    '7*x0 + 11*x1 >= 74',
    '5*x0 + 8*x1 >= 11',
    '2*x0 + 8*x1 >= 34',
    '-4*x0 + 5*x1 >= 0',
    '2*x0 + 15*x1 <= 82',
    '7*x0 + 11*x1 <= 111',
    '5*x0 + 8*x1 <= 49',
    '2*x0 + 8*x1 <= 98'
]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="milligrams_of_vitamin_K", lb=0)  # Assuming non-negative
    x1 = model.addVar(name="milligrams_of_vitamin_A", lb=0)  # Assuming non-negative

    # Define the objective function
    model.setObjective(9 * x0 + 2 * x1, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(2 * x0 + 15 * x1 >= 62)
    model.addConstr(7 * x0 + 11 * x1 >= 74)
    model.addConstr(5 * x0 + 8 * x1 >= 11)
    model.addConstr(2 * x0 + 8 * x1 >= 34)
    model.addConstr(-4 * x0 + 5 * x1 >= 0)
    model.addConstr(2 * x0 + 15 * x1 <= 82)
    model.addConstr(7 * x0 + 11 * x1 <= 111)
    model.addConstr(5 * x0 + 8 * x1 <= 49)
    model.addConstr(2 * x0 + 8 * x1 <= 98)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin K: {x0.varValue}")
        print(f"Milligrams of vitamin A: {x1.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```