## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin E' and 'milligrams of zinc'. Let's denote 'milligrams of vitamin E' as $x_1$ and 'milligrams of zinc' as $x_2$. The objective function to minimize is $1.63x_1 + 8.5x_2$. The constraints are:
- $0.6x_1 \geq 0$ (always true since $x_1 \geq 0$)
- $1.24x_1 \geq 0$ (always true since $x_1 \geq 0$)
- $1.85x_2 \geq 0$ (always true since $x_2 \geq 0$)
- $1.83x_2 \geq 0$ (always true since $x_2 \geq 0$)
- $0.6x_1 + 1.85x_2 \geq 21$
- $0.6x_1 + 1.85x_2 \leq 33$
- $1.24x_1 + 1.83x_2 \geq 21$
- $1.24x_1 + 1.83x_2 \leq 26$
- $7x_1 - 5x_2 \geq 0$

## 2: Convert the problem into a Gurobi-compatible format
We need to express the problem in a way that Gurobi can understand. This involves defining the variables, the objective function, and the constraints.

## 3: Write down the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin E'), ('x2', 'milligrams of zinc')],
'objective_function': '1.63*x1 + 8.5*x2',
'constraints': [
    '0.6*x1 + 1.85*x2 >= 21',
    '0.6*x1 + 1.85*x2 <= 33',
    '1.24*x1 + 1.83*x2 >= 21',
    '1.24*x1 + 1.83*x2 <= 26',
    '7*x1 - 5*x2 >= 0',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem using Gurobi in Python.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="milligrams_of_vitamin_E")
    x2 = model.addVar(lb=0, name="milligrams_of_zinc")

    # Define the objective function
    model.setObjective(1.63 * x1 + 8.5 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(0.6 * x1 + 1.85 * x2 >= 21, name="muscle_growth_index_min")
    model.addConstr(0.6 * x1 + 1.85 * x2 <= 33, name="muscle_growth_index_max")
    model.addConstr(1.24 * x1 + 1.83 * x2 >= 21, name="immune_support_index_min")
    model.addConstr(1.24 * x1 + 1.83 * x2 <= 26, name="immune_support_index_max")
    model.addConstr(7 * x1 - 5 * x2 >= 0, name="vitamin_zinc_balance")

    # Optimize the model
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin E: {x1.varValue}")
        print(f"Milligrams of zinc: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```