## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B9', 'milligrams of calcium', 'milligrams of vitamin B3', 'milligrams of vitamin B2']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $9x_0 + 3x_1 + 8x_2 + 4x_3$.

## Step 3: List all the constraints
The constraints are:
- $7x_0 \leq 296$
- $2x_0 \leq 253$
- $11x_0 \leq 70$
- $4x_1 \leq 296$
- $5x_1 \leq 253$
- $3x_1 \leq 70$
- $10x_2 \leq 296$
- $x_2 \leq 253$
- $2x_2 \leq 70$
- $6x_3 \leq 296$
- $x_3 \leq 253$
- $10x_3 \leq 70$
- $4x_1 + 6x_3 \geq 60$
- $7x_0 + 6x_3 \geq 35$
- $7x_0 + 10x_2 + 6x_3 \geq 67$
- $4x_1 + 10x_2 \geq 24$
- $7x_0 + 4x_1 + 6x_3 \geq 73$
- $7x_0 + 10x_2 + 6x_3 \geq 73$
- $4x_1 + 10x_2 + 6x_3 \geq 73$
- $7x_0 + 4x_1 + 6x_3 \geq 56$
- $7x_0 + 10x_2 + 6x_3 \geq 56$
- $4x_1 + 10x_2 + 6x_3 \geq 56$
- $7x_0 + 4x_1 + 6x_3 \geq 47$
- $7x_0 + 10x_2 + 6x_3 \geq 47$
- $4x_1 + 10x_2 + 6x_3 \geq 47$
- $7x_0 + 4x_1 + 10x_2 + 6x_3 \geq 47$
- $2x_0 + x_2 \geq 30$
- $2x_0 + x_3 \geq 37$
- $2x_0 + 5x_1 + x_3 \geq 41$
- $2x_0 + 5x_1 + x_2 + x_3 \geq 41$
- $3x_1 + 2x_2 \geq 15$
- $11x_0 + 10x_3 \geq 7$
- $11x_0 + 3x_1 \geq 10$
- $3x_1 + 2x_2 + 10x_3 \geq 12$
- $11x_0 + 3x_1 + 2x_2 \geq 12$
- $3x_1 + 2x_2 + 10x_3 \geq 12$
- $11x_0 + 3x_1 + 2x_2 \geq 12$
- $11x_0 + 3x_1 + 2x_2 + 10x_3 \geq 12$
- $4x_0 - 6x_1 \geq 0$
- $4x_1 - 4x_2 \geq 0$
- $4x_1 + 10x_2 \leq 265$
- $7x_0 + 6x_3 \leq 114$
- $10x_2 + 6x_3 \leq 142$
- $5x_1 + x_2 \leq 236$
- $2x_0 + x_2 \leq 196$
- $5x_1 + x_3 \leq 133$
- $11x_0 + 2x_2 \leq 20$

## 4: Write down the gurobi code
```python
import gurobi

# Define the model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="x0", lb=0)  # milligrams of vitamin B9
x1 = model.addVar(name="x1", lb=0)  # milligrams of calcium
x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B3
x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B2

# Define the objective function
model.setObjective(9 * x0 + 3 * x1 + 8 * x2 + 4 * x3, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(7 * x0 <= 296)
model.addConstr(2 * x0 <= 253)
model.addConstr(11 * x0 <= 70)

model.addConstr(4 * x1 <= 296)
model.addConstr(5 * x1 <= 253)
model.addConstr(3 * x1 <= 70)

model.addConstr(10 * x2 <= 296)
model.addConstr(x2 <= 253)
model.addConstr(2 * x2 <= 70)

model.addConstr(6 * x3 <= 296)
model.addConstr(x3 <= 253)
model.addConstr(10 * x3 <= 70)

model.addConstr(4 * x1 + 6 * x3 >= 60)
model.addConstr(7 * x0 + 6 * x3 >= 35)
model.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 67)
model.addConstr(4 * x1 + 10 * x2 >= 24)
model.addConstr(7 * x0 + 4 * x1 + 6 * x3 >= 73)
model.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 73)
model.addConstr(4 * x1 + 10 * x2 + 6 * x3 >= 73)
model.addConstr(7 * x0 + 4 * x1 + 6 * x3 >= 56)
model.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 56)
model.addConstr(4 * x1 + 10 * x2 + 6 * x3 >= 56)
model.addConstr(7 * x0 + 4 * x1 + 6 * x3 >= 47)
model.addConstr(7 * x0 + 10 * x2 + 6 * x3 >= 47)
model.addConstr(4 * x1 + 10 * x2 + 6 * x3 >= 47)
model.addConstr(7 * x0 + 4 * x1 + 10 * x2 + 6 * x3 >= 47)

model.addConstr(2 * x0 + x2 >= 30)
model.addConstr(2 * x0 + x3 >= 37)
model.addConstr(2 * x0 + 5 * x1 + x3 >= 41)
model.addConstr(2 * x0 + 5 * x1 + x2 + x3 >= 41)

model.addConstr(3 * x1 + 2 * x2 >= 15)
model.addConstr(11 * x0 + 10 * x3 >= 7)
model.addConstr(11 * x0 + 3 * x1 >= 10)
model.addConstr(3 * x1 + 2 * x2 + 10 * x3 >= 12)
model.addConstr(11 * x0 + 3 * x1 + 2 * x2 >= 12)
model.addConstr(3 * x1 + 2 * x2 + 10 * x3 >= 12)
model.addConstr(11 * x0 + 3 * x1 + 2 * x2 >= 12)
model.addConstr(11 * x0 + 3 * x1 + 2 * x2 + 10 * x3 >= 12)

model.addConstr(4 * x0 - 6 * x1 >= 0)
model.addConstr(4 * x1 - 4 * x2 >= 0)

model.addConstr(4 * x1 + 10 * x2 <= 265)
model.addConstr(7 * x0 + 6 * x3 <= 114)
model.addConstr(10 * x2 + 6 * x3 <= 142)
model.addConstr(5 * x1 + x2 <= 236)
model.addConstr(2 * x0 + x2 <= 196)
model.addConstr(5 * x1 + x3 <= 133)
model.addConstr(11 * x0 + 2 * x2 <= 20)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
else:
    print("The model is infeasible")
```

## Step 5: Symbolic representation
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B9'), 
        ('x1', 'milligrams of calcium'), 
        ('x2', 'milligrams of vitamin B3'), 
        ('x3', 'milligrams of vitamin B2')
    ], 
    'objective_function': '9*x0 + 3*x1 + 8*x2 + 4*x3', 
    'constraints': [
        '7*x0 <= 296',
        '2*x0 <= 253',
        '11*x0 <= 70',
        '4*x1 <= 296',
        '5*x1 <= 253',
        '3*x1 <= 70',
        '10*x2 <= 296',
        'x2 <= 253',
        '2*x2 <= 70',
        '6*x3 <= 296',
        'x3 <= 253',
        '10*x3 <= 70',
        '4*x1 + 6*x3 >= 60',
        '7*x0 + 6*x3 >= 35',
        '7*x0 + 10*x2 + 6*x3 >= 67',
        '4*x1 + 10*x2 >= 24',
        '7*x0 + 4*x1 + 6*x3 >= 73',
        '7*x0 + 10*x2 + 6*x3 >= 73',
        '4*x1 + 10*x2 + 6*x3 >= 73',
        '7*x0 + 4*x1 + 6*x3 >= 56',
        '7*x0 + 10*x2 + 6*x3 >= 56',
        '4*x1 + 10*x2 + 6*x3 >= 56',
        '7*x0 + 4*x1 + 6*x3 >= 47',
        '7*x0 + 10*x2 + 6*x3 >= 47',
        '4*x1 + 10*x2 + 6*x3 >= 47',
        '7*x0 + 4*x1 + 10*x2 + 6*x3 >= 47',
        '2*x0 + x2 >= 30',
        '2*x0 + x3 >= 37',
        '2*x0 + 5*x1 + x3 >= 41',
        '2*x0 + 5*x1 + x2 + x3 >= 41',
        '3*x1 + 2*x2 >= 15',
        '11*x0 + 10*x3 >= 7',
        '11*x0 + 3*x1 >= 10',
        '3*x1 + 2*x2 + 10*x3 >= 12',
        '11*x0 + 3*x1 + 2*x2 >= 12',
        '3*x1 + 2*x2 + 10*x3 >= 12',
        '11*x0 + 3*x1 + 2*x2 >= 12',
        '11*x0 + 3*x1 + 2*x2 + 10*x3 >= 12',
        '4*x0 - 6*x1 >= 0',
        '4*x1 - 4*x2 >= 0',
        '4*x1 + 10*x2 <= 265',
        '7*x0 + 6*x3 <= 114',
        '10*x2 + 6*x3 <= 142',
        '5*x1 + x2 <= 236',
        '2*x0 + x2 <= 196',
        '5*x1 + x3 <= 133',
        '11*x0 + 2*x2 <= 20'
    ]
}
```