## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of potassium', 'milligrams of vitamin B12', 'milligrams of vitamin A'], which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of potassium
- $x_1$ : milligrams of vitamin B12
- $x_2$ : milligrams of vitamin A

## 3: Define the objective function in symbolic notation
The objective function to maximize is $7.57x_0 + 7.11x_1 + 7.21x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $5x_0 \leq 173$
- $5x_0 \leq 132$
- $1x_0 \leq 203$
- $17x_0 \leq 107$
- $18x_1 \leq 173$
- $11x_1 \leq 132$
- $13x_1 \leq 203$
- $12x_1 \leq 107$
- $7x_2 \leq 173$
- $18x_2 \leq 132$
- $16x_2 \leq 203$
- $20x_2 \leq 107$
- $5x_0 + 11x_1 + 18x_2 \geq 38$
- $1x_0 + 16x_2 \geq 59$
- $1x_0 + 13x_1 + 16x_2 \geq 62$
- $5x_0 + 7x_2 \leq 102$
- $18x_1 + 7x_2 \leq 122$
- $5x_0 + 18x_1 + 7x_2 \leq 157$
- $5x_0 + 18x_1 + 7x_2 \leq 157$
- $5x_0 + 18x_2 \leq 110$
- $11x_1 + 18x_2 \leq 108$
- $5x_0 + 11x_1 + 18x_2 \leq 108$
- $13x_1 + 16x_2 \leq 98$
- $1x_0 + 13x_1 \leq 83$
- $1x_0 + 13x_1 + 16x_2 \leq 83$
- $17x_0 + 12x_1 \leq 107$
- $17x_0 + 20x_2 \leq 49$
- $17x_0 + 12x_1 + 20x_2 \leq 107$
- $17x_0 + 12x_1 + 20x_2 \leq 107$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of potassium'), 
        ('x1', 'milligrams of vitamin B12'), 
        ('x2', 'milligrams of vitamin A')
    ], 
    'objective_function': '7.57*x0 + 7.11*x1 + 7.21*x2', 
    'constraints': [
        '5*x0 <= 173', '5*x0 <= 132', '1*x0 <= 203', '17*x0 <= 107',
        '18*x1 <= 173', '11*x1 <= 132', '13*x1 <= 203', '12*x1 <= 107',
        '7*x2 <= 173', '18*x2 <= 132', '16*x2 <= 203', '20*x2 <= 107',
        '5*x0 + 11*x1 + 18*x2 >= 38', '1*x0 + 16*x2 >= 59', '1*x0 + 13*x1 + 16*x2 >= 62',
        '5*x0 + 7*x2 <= 102', '18*x1 + 7*x2 <= 122', '5*x0 + 18*x1 + 7*x2 <= 157',
        '5*x0 + 18*x1 + 7*x2 <= 157', '5*x0 + 18*x2 <= 110', '11*x1 + 18*x2 <= 108',
        '5*x0 + 11*x1 + 18*x2 <= 108', '13*x1 + 16*x2 <= 98', '1*x0 + 13*x1 <= 83',
        '1*x0 + 13*x1 + 16*x2 <= 83', '17*x0 + 12*x1 <= 107', '17*x0 + 20*x2 <= 49',
        '17*x0 + 12*x1 + 20*x2 <= 107', '17*x0 + 12*x1 + 20*x2 <= 107'
    ]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # milligrams of potassium
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B12
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin A

    # Objective function
    model.setObjective(7.57 * x0 + 7.11 * x1 + 7.21 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5 * x0 <= 173)
    model.addConstr(5 * x0 <= 132)
    model.addConstr(x0 <= 203)
    model.addConstr(17 * x0 <= 107)

    model.addConstr(18 * x1 <= 173)
    model.addConstr(11 * x1 <= 132)
    model.addConstr(13 * x1 <= 203)
    model.addConstr(12 * x1 <= 107)

    model.addConstr(7 * x2 <= 173)
    model.addConstr(18 * x2 <= 132)
    model.addConstr(16 * x2 <= 203)
    model.addConstr(20 * x2 <= 107)

    model.addConstr(5 * x0 + 11 * x1 + 18 * x2 >= 38)
    model.addConstr(x0 + 16 * x2 >= 59)
    model.addConstr(x0 + 13 * x1 + 16 * x2 >= 62)

    model.addConstr(5 * x0 + 7 * x2 <= 102)
    model.addConstr(18 * x1 + 7 * x2 <= 122)
    model.addConstr(5 * x0 + 18 * x1 + 7 * x2 <= 157)

    model.addConstr(5 * x0 + 18 * x2 <= 110)
    model.addConstr(11 * x1 + 18 * x2 <= 108)
    model.addConstr(5 * x0 + 11 * x1 + 18 * x2 <= 108)

    model.addConstr(13 * x1 + 16 * x2 <= 98)
    model.addConstr(x0 + 13 * x1 <= 83)
    model.addConstr(x0 + 13 * x1 + 16 * x2 <= 83)

    model.addConstr(17 * x0 + 12 * x1 <= 107)
    model.addConstr(17 * x0 + 20 * x2 <= 49)
    model.addConstr(17 * x0 + 12 * x1 + 20 * x2 <= 107)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of potassium: {x0.varValue}")
        print(f"Milligrams of vitamin B12: {x1.varValue}")
        print(f"Milligrams of vitamin A: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```