## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B3', 'grams of carbohydrates', and 'milligrams of iron'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $4.93x_1 + 9.47x_2 + 2.7x_3$.

## Step 3: List all the constraints
The constraints given are:
- $8x_1 + 17x_2 + 11x_3 \geq 26$
- $8x_1 + 17x_2 + 11x_3 \geq 45$ (Redundant with the previous one, as 26 < 45)
- $15x_1 + 17x_2 + 22x_3 \geq 21$
- $19x_1 + 8x_2 + 12x_3 \geq 40$
- $19x_1 + 8x_2 + 12x_3 \geq 40$ (Redundant with itself)
- $8x_1 + 17x_2 + 11x_3 \geq 45$
- $15x_1 + 22x_3 \geq 7$
- $15x_1 + 17x_2 \geq 21$
- $15x_1 + 17x_2 + 22x_3 \geq 21$
- $x_1 - 7x_2 \geq 0$
- $-7x_2 + 3x_3 \geq 0$
- $8x_1 + 17x_2 + 11x_3 \leq 109$
- $15x_1 + 22x_3 \leq 63$
- $15x_1 + 17x_2 \leq 64$

## 4: Remove redundant constraints and organize
After removing redundant constraints, we have:
- $x_1 \geq 0$ (Implicit, as there's no lower bound given)
- $x_2 \geq 0$ (Implicit, as there's no lower bound given)
- $x_3 \geq 0$ (Implicit, as there's no lower bound given)
- $8x_1 + 17x_2 + 11x_3 \geq 45$
- $19x_1 + 8x_2 + 12x_3 \geq 40$
- $15x_1 + 17x_2 \geq 21$
- $15x_1 + 22x_3 \geq 7$
- $x_1 - 7x_2 \geq 0$
- $-7x_2 + 3x_3 \geq 0$
- $8x_1 + 17x_2 + 11x_3 \leq 109$
- $15x_1 + 22x_3 \leq 63$
- $15x_1 + 17x_2 \leq 64$

## 5: Convert to Gurobi code
```python
import gurobi

def optimize():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="milligrams_of_vitamin_B3", lb=0)
    x2 = model.addVar(name="grams_of_carbohydrates", lb=0)
    x3 = model.addVar(name="milligrams_of_iron", lb=0)

    # Objective function
    model.setObjective(4.93*x1 + 9.47*x2 + 2.7*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(8*x1 + 17*x2 + 11*x3 >= 45)
    model.addConstr(19*x1 + 8*x2 + 12*x3 >= 40)
    model.addConstr(15*x1 + 17*x2 >= 21)
    model.addConstr(15*x1 + 22*x3 >= 7)
    model.addConstr(x1 - 7*x2 >= 0)
    model.addConstr(-7*x2 + 3*x3 >= 0)
    model.addConstr(8*x1 + 17*x2 + 11*x3 <= 109)
    model.addConstr(15*x1 + 22*x3 <= 63)
    model.addConstr(15*x1 + 17*x2 <= 64)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Milligrams of vitamin B3: ", x1.varValue)
        print("Grams of carbohydrates: ", x2.varValue)
        print("Milligrams of iron: ", x3.varValue)
    else:
        print("No optimal solution found")

optimize()
```

## 6: Symbolic Representation
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B3'),
        ('x2', 'grams of carbohydrates'),
        ('x3', 'milligrams of iron')
    ],
    'objective_function': '4.93*x1 + 9.47*x2 + 2.7*x3',
    'constraints': [
        '8*x1 + 17*x2 + 11*x3 >= 45',
        '19*x1 + 8*x2 + 12*x3 >= 40',
        '15*x1 + 17*x2 >= 21',
        '15*x1 + 22*x3 >= 7',
        'x1 - 7*x2 >= 0',
        '-7*x2 + 3*x3 >= 0',
        '8*x1 + 17*x2 + 11*x3 <= 109',
        '15*x1 + 22*x3 <= 63',
        '15*x1 + 17*x2 <= 64'
    ]
}
```