## Step 1: Define the symbolic representation of the variables
The variables are 'steaks', 'slices of pizza', 'chicken drumsticks', and 'corn cobs', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is $9x_0 + 6x_1 + 1x_2 + 3x_3$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $2.73x_0 + 6.21x_1 + 8.01x_2 + 6.38x_3 \leq 178$ (iron upper bound)
- $2.53x_0 + 1.96x_1 + 0.64x_2 + 2.58x_3 \leq 220$ (sourness index upper bound)
- $8.01x_2 + 6.38x_3 \geq 16$ (iron from chicken drumsticks and corn cobs)
- $2.73x_0 + 8.01x_2 \geq 28$ (iron from steaks and chicken drumsticks)
- $2.73x_0 + 6.21x_1 \geq 14$ (iron from steaks and slices of pizza)
- $6.21x_1 + 8.01x_2 \geq 26$ (iron from slices of pizza and chicken drumsticks)
- $2.73x_0 + 6.38x_3 \geq 29$ (iron from steaks and corn cobs)
- $2.73x_0 + 6.21x_1 + 8.01x_2 + 6.38x_3 \geq 29$ (total iron from all)
- $2.53x_0 + 2.58x_3 \geq 32$ (sourness index from steaks and corn cobs)
- $0.64x_2 + 2.58x_3 \geq 21$ (sourness index from chicken drumsticks and corn cobs)
- $2.53x_0 + 1.96x_1 \geq 25$ (sourness index from steaks and slices of pizza)
- $1.96x_1 + 2.58x_3 \geq 23$ (sourness index from slices of pizza and corn cobs)
- $1.96x_1 + 0.64x_2 \geq 25$ (sourness index from slices of pizza and chicken drumsticks)
- $2.53x_0 + 1.96x_1 + 0.64x_2 + 2.58x_3 \geq 25$ (total sourness index from all)
- $2x_0 - 7x_3 \geq 0$ (steaks and corn cobs relation)
- $2.73x_0 + 6.21x_1 \leq 139$ (iron from steaks and slices of pizza upper bound)
- $6.21x_1 + 6.38x_3 \leq 49$ (iron from slices of pizza and corn cobs upper bound)
- $2.53x_0 + 1.96x_1 \leq 107$ (sourness index from steaks and slices of pizza upper bound)
- $0.64x_2 + 2.58x_3 \leq 91$ (sourness index from chicken drumsticks and corn cobs upper bound)

## Step 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'steaks'), 
        ('x1', 'slices of pizza'), 
        ('x2', 'chicken drumsticks'), 
        ('x3', 'corn cobs')
    ], 
    'objective_function': '9*x0 + 6*x1 + 1*x2 + 3*x3', 
    'constraints': [
        '2.73*x0 + 6.21*x1 + 8.01*x2 + 6.38*x3 <= 178',
        '2.53*x0 + 1.96*x1 + 0.64*x2 + 2.58*x3 <= 220',
        '8.01*x2 + 6.38*x3 >= 16',
        '2.73*x0 + 8.01*x2 >= 28',
        '2.73*x0 + 6.21*x1 >= 14',
        '6.21*x1 + 8.01*x2 >= 26',
        '2.73*x0 + 6.38*x3 >= 29',
        '2.73*x0 + 6.21*x1 + 8.01*x2 + 6.38*x3 >= 29',
        '2.53*x0 + 2.58*x3 >= 32',
        '0.64*x2 + 2.58*x3 >= 21',
        '2.53*x0 + 1.96*x1 >= 25',
        '1.96*x1 + 2.58*x3 >= 23',
        '1.96*x1 + 0.64*x2 >= 25',
        '2.53*x0 + 1.96*x1 + 0.64*x2 + 2.58*x3 >= 25',
        '2*x0 - 7*x3 >= 0',
        '2.73*x0 + 6.21*x1 <= 139',
        '6.21*x1 + 6.38*x3 <= 49',
        '2.53*x0 + 1.96*x1 <= 107',
        '0.64*x2 + 2.58*x3 <= 91'
    ]
}
```

## Step 5: Write the Gurobi code for the optimization problem
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name='steaks', lb=0)
x1 = model.addVar(name='slices of pizza', lb=0)
x2 = model.addVar(name='chicken drumsticks', lb=0)
x3 = model.addVar(name='corn cobs', lb=0)

# Set the objective function
model.setObjective(9 * x0 + 6 * x1 + x2 + 3 * x3, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(2.73 * x0 + 6.21 * x1 + 8.01 * x2 + 6.38 * x3 <= 178)
model.addConstr(2.53 * x0 + 1.96 * x1 + 0.64 * x2 + 2.58 * x3 <= 220)
model.addConstr(8.01 * x2 + 6.38 * x3 >= 16)
model.addConstr(2.73 * x0 + 8.01 * x2 >= 28)
model.addConstr(2.73 * x0 + 6.21 * x1 >= 14)
model.addConstr(6.21 * x1 + 8.01 * x2 >= 26)
model.addConstr(2.73 * x0 + 6.38 * x3 >= 29)
model.addConstr(2.73 * x0 + 6.21 * x1 + 8.01 * x2 + 6.38 * x3 >= 29)
model.addConstr(2.53 * x0 + 2.58 * x3 >= 32)
model.addConstr(0.64 * x2 + 2.58 * x3 >= 21)
model.addConstr(2.53 * x0 + 1.96 * x1 >= 25)
model.addConstr(1.96 * x1 + 2.58 * x3 >= 23)
model.addConstr(1.96 * x1 + 0.64 * x2 >= 25)
model.addConstr(2.53 * x0 + 1.96 * x1 + 0.64 * x2 + 2.58 * x3 >= 25)
model.addConstr(2 * x0 - 7 * x3 >= 0)
model.addConstr(2.73 * x0 + 6.21 * x1 <= 139)
model.addConstr(6.21 * x1 + 6.38 * x3 <= 49)
model.addConstr(2.53 * x0 + 1.96 * x1 <= 107)
model.addConstr(0.64 * x2 + 2.58 * x3 <= 91)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objval)
    print('Steaks: ', x0.varValue)
    print('Slices of pizza: ', x1.varValue)
    print('Chicken drumsticks: ', x2.varValue)
    print('Corn cobs: ', x3.varValue)
else:
    print('The model is infeasible')
```