## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of zinc', 'milligrams of vitamin B1', and 'milligrams of calcium', 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 and their corresponding natural language objects are:
- $x_0$ : milligrams of zinc
- $x_1$ : milligrams of vitamin B1
- $x_2$ : milligrams of calcium

## 3: Define the objective function in symbolic notation
The objective function to maximize is $7.43x_0 + 2.24x_1 + 4.55x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $4x_0 \leq 236$
- $9x_0 \leq 212$
- $8x_0 \leq 191$
- $13x_1 \leq 236$
- $6x_1 \leq 212$
- $21x_1 \leq 191$
- $24x_2 \leq 236$
- $28x_2 \leq 212$
- $27x_2 \leq 191$
- $13x_1 + 24x_2 \geq 53$
- $8x_0 + 27x_2 \geq 21$
- $13x_1 + 24x_2 \leq 176$
- $4x_0 + 13x_1 + 24x_2 \leq 176$
- $6x_1 + 28x_2 \leq 93$
- $9x_0 + 6x_1 \leq 210$
- $9x_0 + 28x_2 \leq 187$
- $9x_0 + 6x_1 + 28x_2 \leq 187$
- $8x_0 + 21x_1 \leq 131$
- $8x_0 + 27x_2 \leq 147$
- $8x_0 + 21x_1 + 27x_2 \leq 147$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'milligrams of zinc'), ('x1', 'milligrams of vitamin B1'), ('x2', 'milligrams of calcium')],
    'objective_function': '7.43x0 + 2.24x1 + 4.55x2',
    'constraints': [
        '4x0 <= 236',
        '9x0 <= 212',
        '8x0 <= 191',
        '13x1 <= 236',
        '6x1 <= 212',
        '21x1 <= 191',
        '24x2 <= 236',
        '28x2 <= 212',
        '27x2 <= 191',
        '13x1 + 24x2 >= 53',
        '8x0 + 27x2 >= 21',
        '13x1 + 24x2 <= 176',
        '4x0 + 13x1 + 24x2 <= 176',
        '6x1 + 28x2 <= 93',
        '9x0 + 6x1 <= 210',
        '9x0 + 28x2 <= 187',
        '9x0 + 6x1 + 28x2 <= 187',
        '8x0 + 21x1 <= 131',
        '8x0 + 27x2 <= 147',
        '8x0 + 21x1 + 27x2 <= 147'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize():
    # 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_B1", lb=0)
    x2 = model.addVar(name="milligrams_of_calcium", lb=0)

    # Define the objective function
    model.setObjective(7.43 * x0 + 2.24 * x1 + 4.55 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(4 * x0 <= 236)
    model.addConstr(9 * x0 <= 212)
    model.addConstr(8 * x0 <= 191)
    model.addConstr(13 * x1 <= 236)
    model.addConstr(6 * x1 <= 212)
    model.addConstr(21 * x1 <= 191)
    model.addConstr(24 * x2 <= 236)
    model.addConstr(28 * x2 <= 212)
    model.addConstr(27 * x2 <= 191)
    model.addConstr(13 * x1 + 24 * x2 >= 53)
    model.addConstr(8 * x0 + 27 * x2 >= 21)
    model.addConstr(13 * x1 + 24 * x2 <= 176)
    model.addConstr(4 * x0 + 13 * x1 + 24 * x2 <= 176)
    model.addConstr(6 * x1 + 28 * x2 <= 93)
    model.addConstr(9 * x0 + 6 * x1 <= 210)
    model.addConstr(9 * x0 + 28 * x2 <= 187)
    model.addConstr(9 * x0 + 6 * x1 + 28 * x2 <= 187)
    model.addConstr(8 * x0 + 21 * x1 <= 131)
    model.addConstr(8 * x0 + 27 * x2 <= 147)
    model.addConstr(8 * x0 + 21 * x1 + 27 * x2 <= 147)

    # Solve 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 B1: {x1.varValue}")
        print(f"Milligrams of calcium: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize()
```