## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fiber', 'milligrams of vitamin B4', and 'milligrams of vitamin B5', 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:
- $x_0$ : grams of fiber
- $x_1$ : milligrams of vitamin B4
- $x_2$ : milligrams of vitamin B5

## 3: Define the objective function in symbolic notation
The objective function to maximize is $1x_0 + 7x_1 + 5x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $21x_0 \leq 365$
- $23x_0 \leq 255$
- $14x_0 \leq 387$
- $8x_0 \leq 530$
- $13x_0 \leq 396$
- $30x_1 \leq 365$
- $28x_1 \leq 255$
- $14x_1 \leq 387$
- $34x_1 \leq 530$
- $29x_1 \leq 396$
- $26x_2 \leq 365$
- $31x_2 \leq 255$
- $19x_2 \leq 387$
- $20x_2 \leq 530$
- $15x_2 \leq 396$
- $34x_1 + 20x_2 \geq 103$
- $13x_0 + 29x_1 \geq 59$
- $21x_0 + 26x_2 \leq 353$
- $21x_0 + 30x_1 + 26x_2 \leq 353$
- $23x_0 + 31x_2 \leq 148$
- $28x_1 + 31x_2 \leq 130$
- $23x_0 + 28x_1 + 31x_2 \leq 130$
- $14x_0 + 19x_2 \leq 357$
- $14x_0 + 14x_1 \leq 261$
- $14x_0 + 14x_1 + 19x_2 \leq 261$
- $34x_1 + 20x_2 \leq 517$
- $8x_0 + 20x_2 \leq 301$
- $8x_0 + 34x_1 + 20x_2 \leq 301$
- $13x_0 + 29x_1 \leq 386$
- $13x_0 + 15x_2 \leq 367$
- $13x_0 + 29x_1 + 15x_2 \leq 367$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'grams of fiber'), 
        ('x1', 'milligrams of vitamin B4'), 
        ('x2', 'milligrams of vitamin B5')
    ], 
    'objective_function': '1*x0 + 7*x1 + 5*x2', 
    'constraints': [
        '21*x0 <= 365', 
        '23*x0 <= 255', 
        '14*x0 <= 387', 
        '8*x0 <= 530', 
        '13*x0 <= 396', 
        '30*x1 <= 365', 
        '28*x1 <= 255', 
        '14*x1 <= 387', 
        '34*x1 <= 530', 
        '29*x1 <= 396', 
        '26*x2 <= 365', 
        '31*x2 <= 255', 
        '19*x2 <= 387', 
        '20*x2 <= 530', 
        '15*x2 <= 396', 
        '34*x1 + 20*x2 >= 103', 
        '13*x0 + 29*x1 >= 59', 
        '21*x0 + 26*x2 <= 353', 
        '21*x0 + 30*x1 + 26*x2 <= 353', 
        '23*x0 + 31*x2 <= 148', 
        '28*x1 + 31*x2 <= 130', 
        '23*x0 + 28*x1 + 31*x2 <= 130', 
        '14*x0 + 19*x2 <= 357', 
        '14*x0 + 14*x1 <= 261', 
        '14*x0 + 14*x1 + 19*x2 <= 261', 
        '34*x1 + 20*x2 <= 517', 
        '8*x0 + 20*x2 <= 301', 
        '8*x0 + 34*x1 + 20*x2 <= 301', 
        '13*x0 + 29*x1 <= 386', 
        '13*x0 + 15*x2 <= 367', 
        '13*x0 + 29*x1 + 15*x2 <= 367'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=-float('inf'), ub=float('inf'))
    x1 = model.addVar(name="x1", lb=-float('inf'), ub=float('inf'))
    x2 = model.addVar(name="x2", lb=-float('inf'), ub=float('inf'))

    # Define the objective function
    model.setObjective(1 * x0 + 7 * x1 + 5 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(21 * x0 <= 365)
    model.addConstr(23 * x0 <= 255)
    model.addConstr(14 * x0 <= 387)
    model.addConstr(8 * x0 <= 530)
    model.addConstr(13 * x0 <= 396)
    model.addConstr(30 * x1 <= 365)
    model.addConstr(28 * x1 <= 255)
    model.addConstr(14 * x1 <= 387)
    model.addConstr(34 * x1 <= 530)
    model.addConstr(29 * x1 <= 396)
    model.addConstr(26 * x2 <= 365)
    model.addConstr(31 * x2 <= 255)
    model.addConstr(19 * x2 <= 387)
    model.addConstr(20 * x2 <= 530)
    model.addConstr(15 * x2 <= 396)
    model.addConstr(34 * x1 + 20 * x2 >= 103)
    model.addConstr(13 * x0 + 29 * x1 >= 59)
    model.addConstr(21 * x0 + 26 * x2 <= 353)
    model.addConstr(21 * x0 + 30 * x1 + 26 * x2 <= 353)
    model.addConstr(23 * x0 + 31 * x2 <= 148)
    model.addConstr(28 * x1 + 31 * x2 <= 130)
    model.addConstr(23 * x0 + 28 * x1 + 31 * x2 <= 130)
    model.addConstr(14 * x0 + 19 * x2 <= 357)
    model.addConstr(14 * x0 + 14 * x1 <= 261)
    model.addConstr(14 * x0 + 14 * x1 + 19 * x2 <= 261)
    model.addConstr(34 * x1 + 20 * x2 <= 517)
    model.addConstr(8 * x0 + 20 * x2 <= 301)
    model.addConstr(8 * x0 + 34 * x1 + 20 * x2 <= 301)
    model.addConstr(13 * x0 + 29 * x1 <= 386)
    model.addConstr(13 * x0 + 15 * x2 <= 367)
    model.addConstr(13 * x0 + 29 * x1 + 15 * x2 <= 367)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x0: {x0.varValue}")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```