## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin E', 'grams of fat', 'grams of protein', 'milligrams of vitamin B4']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin E'
- $x_2$ for 'grams of fat'
- $x_3$ for 'grams of protein'
- $x_4$ for 'milligrams of vitamin B4'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $3.58x_1 + 5.69x_2 + 3.28x_3 + 2.48x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $11x_1 \leq 171$
- $14x_2 \leq 171$
- $11x_3 \leq 171$
- $13x_4 \leq 171$
- $11x_1 + 14x_2 + 13x_4 \geq 40$
- $11x_1 + 11x_3 + 13x_4 \geq 40$
- $11x_1 + 14x_2 + 13x_4 \geq 36$
- $11x_1 + 11x_3 + 13x_4 \geq 36$
- $11x_1 + 11x_3 \leq 124$
- $11x_3 + 13x_4 \leq 146$
- $11x_1 + 14x_2 \leq 152$
- $14x_2 + 13x_4 \leq 81$
- $11x_1 + 14x_2 + 11x_3 + 13x_4 \leq 81$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin E'), 
        ('x2', 'grams of fat'), 
        ('x3', 'grams of protein'), 
        ('x4', 'milligrams of vitamin B4')
    ], 
    'objective_function': '3.58*x1 + 5.69*x2 + 3.28*x3 + 2.48*x4', 
    'constraints': [
        '11*x1 <= 171',
        '14*x2 <= 171',
        '11*x3 <= 171',
        '13*x4 <= 171',
        '11*x1 + 14*x2 + 13*x4 >= 40',
        '11*x1 + 11*x3 + 13*x4 >= 40',
        '11*x1 + 14*x2 + 13*x4 >= 36',
        '11*x1 + 11*x3 + 13*x4 >= 36',
        '11*x1 + 11*x3 <= 124',
        '11*x3 + 13*x4 <= 146',
        '11*x1 + 14*x2 <= 152',
        '14*x2 + 13*x4 <= 81',
        '11*x1 + 14*x2 + 11*x3 + 13*x4 <= 81'
    ]
}
```

## Step 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
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name='milligrams of vitamin E', vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name='grams of fat', vtype=gurobi.GRB.CONTINUOUS)
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name='grams of protein', vtype=gurobi.GRB.CONTINUOUS)
    x4 = model.addVar(lb=-gurobi.GRB.INFINITY, name='milligrams of vitamin B4', vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(3.58*x1 + 5.69*x2 + 3.28*x3 + 2.48*x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(11*x1 <= 171)
    model.addConstr(14*x2 <= 171)
    model.addConstr(11*x3 <= 171)
    model.addConstr(13*x4 <= 171)
    model.addConstr(11*x1 + 14*x2 + 13*x4 >= 40)
    model.addConstr(11*x1 + 11*x3 + 13*x4 >= 40)
    model.addConstr(11*x1 + 14*x2 + 13*x4 >= 36)
    model.addConstr(11*x1 + 11*x3 + 13*x4 >= 36)
    model.addConstr(11*x1 + 11*x3 <= 124)
    model.addConstr(11*x3 + 13*x4 <= 146)
    model.addConstr(11*x1 + 14*x2 <= 152)
    model.addConstr(14*x2 + 13*x4 <= 81)
    model.addConstr(11*x1 + 14*x2 + 11*x3 + 13*x4 <= 81)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('x1: ', x1.varValue)
        print('x2: ', x2.varValue)
        print('x3: ', x3.varValue)
        print('x4: ', x4.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```