## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin A', 'milligrams of vitamin D', 'milligrams of vitamin B4', 'milligrams of vitamin B7', 'grams of carbohydrates']. Let's denote them as $x_1, x_2, x_3, x_4, x_5$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : milligrams of vitamin A
- $x_2$ : milligrams of vitamin D
- $x_3$ : milligrams of vitamin B4
- $x_4$ : milligrams of vitamin B7
- $x_5$ : grams of carbohydrates

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $6x_1 + 4x_2 + x_3 + 4x_4 + 5x_5$

## 4: List the constraints in symbolic notation
The constraints are:
- $4x_1 \leq 300$ (Implicitly, $x_1 \geq 0$ but we focus on given constraints)
- $7x_1 \leq 298$
- $14x_2 \leq 300$
- $8x_2 \leq 298$
- $2x_3 \leq 300$
- $5x_3 \leq 298$
- $5x_4 \leq 300$
- $9x_4 \leq 298$
- $8x_5 \leq 300$
- $12x_5 \leq 298$
- $4x_1 + 5x_4 \leq 207$
- $4x_1 + 8x_5 \leq 261$
- $14x_2 + 5x_4 \leq 72$
- $4x_1 + 14x_2 \leq 169$
- $14x_2 + 2x_3 \leq 248$
- $4x_1 + 14x_2 + 5x_4 \leq 269$
- $4x_1 + 14x_2 + 2x_3 \leq 150$
- $4x_1 + 14x_2 + 2x_3 + 5x_4 + 8x_5 \leq 150$
- $7x_1 + 8x_2 \leq 217$
- $8x_2 + 12x_5 \leq 170$
- $9x_4 + 12x_5 \leq 202$
- $7x_1 + 9x_4 \leq 148$
- $7x_1 + 8x_2 + 5x_3 \leq 114$
- $7x_1 + 8x_2 + 9x_4 \leq 148$
- $7x_1 + 8x_2 + 12x_5 \leq 87$
- $7x_1 + 5x_3 + 9x_4 \leq 242$
- $8x_2 + 5x_3 + 9x_4 \leq 254$
- $8x_2 + 5x_3 + 12x_5 \leq 247$
- $7x_1 + 8x_2 + 5x_3 + 9x_4 + 12x_5 \leq 247$

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin A'), 
        ('x2', 'milligrams of vitamin D'), 
        ('x3', 'milligrams of vitamin B4'), 
        ('x4', 'milligrams of vitamin B7'), 
        ('x5', 'grams of carbohydrates')
    ], 
    'objective_function': '6*x1 + 4*x2 + x3 + 4*x4 + 5*x5', 
    'constraints': [
        '4*x1 <= 300',
        '7*x1 <= 298',
        '14*x2 <= 300',
        '8*x2 <= 298',
        '2*x3 <= 300',
        '5*x3 <= 298',
        '5*x4 <= 300',
        '9*x4 <= 298',
        '8*x5 <= 300',
        '12*x5 <= 298',
        '4*x1 + 5*x4 <= 207',
        '4*x1 + 8*x5 <= 261',
        '14*x2 + 5*x4 <= 72',
        '4*x1 + 14*x2 <= 169',
        '14*x2 + 2*x3 <= 248',
        '4*x1 + 14*x2 + 5*x4 <= 269',
        '4*x1 + 14*x2 + 2*x3 <= 150',
        '4*x1 + 14*x2 + 2*x3 + 5*x4 + 8*x5 <= 150',
        '7*x1 + 8*x2 <= 217',
        '8*x2 + 12*x5 <= 170',
        '9*x4 + 12*x5 <= 202',
        '7*x1 + 9*x4 <= 148',
        '7*x1 + 8*x2 + 5*x3 <= 114',
        '7*x1 + 8*x2 + 9*x4 <= 148',
        '7*x1 + 8*x2 + 12*x5 <= 87',
        '7*x1 + 5*x3 + 9*x4 <= 242',
        '8*x2 + 5*x3 + 9*x4 <= 254',
        '8*x2 + 5*x3 + 12*x5 <= 247',
        '7*x1 + 8*x2 + 5*x3 + 9*x4 + 12*x5 <= 247'
    ]
}
```

## 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)  # milligrams of vitamin A
    x2 = model.addVar(name='x2', lb=0)  # milligrams of vitamin D
    x3 = model.addVar(name='x3', lb=0)  # milligrams of vitamin B4
    x4 = model.addVar(name='x4', lb=0)  # milligrams of vitamin B7
    x5 = model.addVar(name='x5', lb=0)  # grams of carbohydrates

    # Define the objective function
    model.setObjective(6 * x1 + 4 * x2 + x3 + 4 * x4 + 5 * x5, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(4 * x1 <= 300)
    model.addConstr(7 * x1 <= 298)
    model.addConstr(14 * x2 <= 300)
    model.addConstr(8 * x2 <= 298)
    model.addConstr(2 * x3 <= 300)
    model.addConstr(5 * x3 <= 298)
    model.addConstr(5 * x4 <= 300)
    model.addConstr(9 * x4 <= 298)
    model.addConstr(8 * x5 <= 300)
    model.addConstr(12 * x5 <= 298)
    model.addConstr(4 * x1 + 5 * x4 <= 207)
    model.addConstr(4 * x1 + 8 * x5 <= 261)
    model.addConstr(14 * x2 + 5 * x4 <= 72)
    model.addConstr(4 * x1 + 14 * x2 <= 169)
    model.addConstr(14 * x2 + 2 * x3 <= 248)
    model.addConstr(4 * x1 + 14 * x2 + 5 * x4 <= 269)
    model.addConstr(4 * x1 + 14 * x2 + 2 * x3 <= 150)
    model.addConstr(4 * x1 + 14 * x2 + 2 * x3 + 5 * x4 + 8 * x5 <= 150)
    model.addConstr(7 * x1 + 8 * x2 <= 217)
    model.addConstr(8 * x2 + 12 * x5 <= 170)
    model.addConstr(9 * x4 + 12 * x5 <= 202)
    model.addConstr(7 * x1 + 9 * x4 <= 148)
    model.addConstr(7 * x1 + 8 * x2 + 5 * x3 <= 114)
    model.addConstr(7 * x1 + 8 * x2 + 9 * x4 <= 148)
    model.addConstr(7 * x1 + 8 * x2 + 12 * x5 <= 87)
    model.addConstr(7 * x1 + 5 * x3 + 9 * x4 <= 242)
    model.addConstr(8 * x2 + 5 * x3 + 9 * x4 <= 254)
    model.addConstr(8 * x2 + 5 * x3 + 12 * x5 <= 247)
    model.addConstr(7 * x1 + 8 * x2 + 5 * x3 + 9 * x4 + 12 * x5 <= 247)

    # 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)
        print("x5: ", x5.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```