## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of zinc', 'milligrams of vitamin B5', 'milligrams of vitamin B7']. Let's denote them 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 zinc
- $x_1$ : milligrams of vitamin B5
- $x_2$ : milligrams of vitamin B7

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $2.4x_0 + 9.41x_1 + 7.62x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $28x_0 \leq 179$
2. $21x_0 \leq 284$
3. $28x_1 \leq 179$
4. $29x_1 \leq 284$
5. $12x_2 \leq 179$
6. $11x_2 \leq 284$
7. $28x_0 + 12x_2 \geq 21$
8. $28x_1 + 12x_2 \geq 22$
9. $28x_0 + 28x_1 + 12x_2 \geq 22$
10. $29x_1 + 11x_2 \geq 33$
11. $21x_0 + 29x_1 \geq 75$
12. $21x_0 + 11x_2 \geq 35$
13. $21x_0 + 29x_1 + 11x_2 \geq 61$
14. $21x_0 + 29x_1 + 11x_2 \geq 61$
15. $-3x_0 + 7x_1 \geq 0$
16. $-10x_0 + 6x_2 \geq 0$
17. $28x_1 + 12x_2 \leq 156$
18. $28x_0 + 28x_1 + 12x_2 \leq 138$
19. $21x_0 + 11x_2 \leq 276$
20. $21x_0 + 29x_1 \leq 203$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [
    ('x0', 'milligrams of zinc'), 
    ('x1', 'milligrams of vitamin B5'), 
    ('x2', 'milligrams of vitamin B7')
],
'objective_function': '2.4*x0 + 9.41*x1 + 7.62*x2',
'constraints': [
    '28*x0 <= 179',
    '21*x0 <= 284',
    '28*x1 <= 179',
    '29*x1 <= 284',
    '12*x2 <= 179',
    '11*x2 <= 284',
    '28*x0 + 12*x2 >= 21',
    '28*x1 + 12*x2 >= 22',
    '28*x0 + 28*x1 + 12*x2 >= 22',
    '29*x1 + 11*x2 >= 33',
    '21*x0 + 29*x1 >= 75',
    '21*x0 + 11*x2 >= 35',
    '21*x0 + 29*x1 + 11*x2 >= 61',
    '21*x0 + 29*x1 + 11*x2 >= 61',
    '-3*x0 + 7*x1 >= 0',
    '-10*x0 + 6*x2 >= 0',
    '28*x1 + 12*x2 <= 156',
    '28*x0 + 28*x1 + 12*x2 <= 138',
    '21*x0 + 11*x2 <= 276',
    '21*x0 + 29*x1 <= 203'
]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="milligrams_of_zinc", lb=0)
    x1 = model.addVar(name="milligrams_of_vitamin_B5", lb=0)
    x2 = model.addVar(name="milligrams_of_vitamin_B7", lb=0)

    # Define the objective function
    model.setObjective(2.4 * x0 + 9.41 * x1 + 7.62 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(28 * x0 <= 179)
    model.addConstr(21 * x0 <= 284)
    model.addConstr(28 * x1 <= 179)
    model.addConstr(29 * x1 <= 284)
    model.addConstr(12 * x2 <= 179)
    model.addConstr(11 * x2 <= 284)
    model.addConstr(28 * x0 + 12 * x2 >= 21)
    model.addConstr(28 * x1 + 12 * x2 >= 22)
    model.addConstr(28 * x0 + 28 * x1 + 12 * x2 >= 22)
    model.addConstr(29 * x1 + 11 * x2 >= 33)
    model.addConstr(21 * x0 + 29 * x1 >= 75)
    model.addConstr(21 * x0 + 11 * x2 >= 35)
    model.addConstr(21 * x0 + 29 * x1 + 11 * x2 >= 61)
    model.addConstr(21 * x0 + 29 * x1 + 11 * x2 >= 61)
    model.addConstr(-3 * x0 + 7 * x1 >= 0)
    model.addConstr(-10 * x0 + 6 * x2 >= 0)
    model.addConstr(28 * x1 + 12 * x2 <= 156)
    model.addConstr(28 * x0 + 28 * x1 + 12 * x2 <= 138)
    model.addConstr(21 * x0 + 11 * x2 <= 276)
    model.addConstr(21 * x0 + 29 * x1 <= 203)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of zinc: {x0.varValue}")
        print(f"Milligrams of vitamin B5: {x1.varValue}")
        print(f"Milligrams of vitamin B7: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```