## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fat', 'milligrams of vitamin B5', and 'milligrams of zinc'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ = 'grams of fat'
- $x_2$ = 'milligrams of vitamin B5'
- $x_3$ = 'milligrams of zinc'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $3.69x_1 + 9.03x_2 + 5.22x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $0.76x_1 \leq 116$
- $0.39x_1 \leq 203$
- $0.92x_1 \leq 99$
- $0.2x_2 \leq 116$
- $0.41x_2 \leq 203$
- $0.1x_2 \leq 99$
- $0.16x_3 \leq 116$
- $0.33x_3 \leq 203$
- $0.7x_3 \leq 99$
- $0.39x_1 + 0.41x_2 \geq 37$
- $0.39x_1 + 0.41x_2 + 0.33x_3 \geq 62$
- $7x_2 - x_3 \geq 0$
- $0.76x_1 + 0.2x_2 \leq 90$
- $0.76x_1 + 0.16x_3 \leq 90$
- $0.76x_1 + 0.2x_2 + 0.16x_3 \leq 58$
- $0.39x_1 + 0.33x_3 \leq 169$
- $0.39x_1 + 0.41x_2 \leq 106$
- $0.41x_2 + 0.33x_3 \leq 161$
- $0.39x_1 + 0.41x_2 + 0.33x_3 \leq 161$
- $0.1x_2 + 0.7x_3 \leq 84$
- $0.92x_1 + 0.7x_3 \leq 67$
- $0.92x_1 + 0.1x_2 + 0.7x_3 \leq 67$
- $x_3 \geq 0$ and $x_3$ is an integer.

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'grams of fat'), 
        ('x2', 'milligrams of vitamin B5'), 
        ('x3', 'milligrams of zinc')
    ], 
    'objective_function': '3.69*x1 + 9.03*x2 + 5.22*x3', 
    'constraints': [
        '0.76*x1 <= 116', 
        '0.39*x1 <= 203', 
        '0.92*x1 <= 99', 
        '0.2*x2 <= 116', 
        '0.41*x2 <= 203', 
        '0.1*x2 <= 99', 
        '0.16*x3 <= 116', 
        '0.33*x3 <= 203', 
        '0.7*x3 <= 99', 
        '0.39*x1 + 0.41*x2 >= 37', 
        '0.39*x1 + 0.41*x2 + 0.33*x3 >= 62', 
        '7*x2 - x3 >= 0', 
        '0.76*x1 + 0.2*x2 <= 90', 
        '0.76*x1 + 0.16*x3 <= 90', 
        '0.76*x1 + 0.2*x2 + 0.16*x3 <= 58', 
        '0.39*x1 + 0.33*x3 <= 169', 
        '0.39*x1 + 0.41*x2 <= 106', 
        '0.41*x2 + 0.33*x3 <= 161', 
        '0.39*x1 + 0.41*x2 + 0.33*x3 <= 161', 
        '0.1*x2 + 0.7*x3 <= 84', 
        '0.92*x1 + 0.7*x3 <= 67', 
        '0.92*x1 + 0.1*x2 + 0.7*x3 <= 67'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # grams of fat
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B5
    x3 = model.addVar(name="x3", lb=0, type=gurobi.GRB.INTEGER)  # milligrams of zinc

    # Define the objective function
    model.setObjective(3.69 * x1 + 9.03 * x2 + 5.22 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(0.76 * x1 <= 116)
    model.addConstr(0.39 * x1 <= 203)
    model.addConstr(0.92 * x1 <= 99)
    model.addConstr(0.2 * x2 <= 116)
    model.addConstr(0.41 * x2 <= 203)
    model.addConstr(0.1 * x2 <= 99)
    model.addConstr(0.16 * x3 <= 116)
    model.addConstr(0.33 * x3 <= 203)
    model.addConstr(0.7 * x3 <= 99)
    model.addConstr(0.39 * x1 + 0.41 * x2 >= 37)
    model.addConstr(0.39 * x1 + 0.41 * x2 + 0.33 * x3 >= 62)
    model.addConstr(7 * x2 - x3 >= 0)
    model.addConstr(0.76 * x1 + 0.2 * x2 <= 90)
    model.addConstr(0.76 * x1 + 0.16 * x3 <= 90)
    model.addConstr(0.76 * x1 + 0.2 * x2 + 0.16 * x3 <= 58)
    model.addConstr(0.39 * x1 + 0.33 * x3 <= 169)
    model.addConstr(0.39 * x1 + 0.41 * x2 <= 106)
    model.addConstr(0.41 * x2 + 0.33 * x3 <= 161)
    model.addConstr(0.39 * x1 + 0.41 * x2 + 0.33 * x3 <= 161)
    model.addConstr(0.1 * x2 + 0.7 * x3 <= 84)
    model.addConstr(0.92 * x1 + 0.7 * x3 <= 67)
    model.addConstr(0.92 * x1 + 0.1 * x2 + 0.7 * x3 <= 67)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Objective: {model.objVal}")
        print(f"Grams of fat: {x1.varValue}")
        print(f"Milligrams of vitamin B5: {x2.varValue}")
        print(f"Milligrams of zinc: {x3.varValue}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```