## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'tomatoes', 'corn cobs', and 'bowls of instant ramen', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $9.48x_0 + 4.93x_1 + 1.09x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $19x_0 + 12x_1 \geq 60$ (total combined grams of protein from tomatoes and corn cobs)
2. $17x_1 + 13x_2 \leq 100$ (milligrams of calcium from corn cobs and bowls of instant ramen)
3. $11x_0 + 17x_1 \leq 132$ (milligrams of calcium from tomatoes and corn cobs)
4. $11x_0 + 13x_2 \leq 77$ (milligrams of calcium from tomatoes and bowls of instant ramen)
5. $11x_0 + 17x_1 + 13x_2 \leq 77$ (total milligrams of calcium from all sources)
6. $12x_1 + 9x_2 \leq 181$ (total combined grams of protein from corn cobs and bowls of instant ramen)
7. $19x_0 + 9x_2 \leq 143$ (total combined grams of protein from tomatoes and bowls of instant ramen)
8. $19x_0 + 12x_1 \leq 183$ (total combined grams of protein from tomatoes and corn cobs)
9. $19x_0 + 12x_1 + 9x_2 \leq 183$ (total combined grams of protein from all sources)

## 4: Define the symbolic variables and constraints for output
The symbolic variables are:
- $x_0$ for 'tomatoes'
- $x_1$ for 'corn cobs'
- $x_2$ for 'bowls of instant ramen'

## 5: Output the symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'tomatoes'), ('x1', 'corn cobs'), ('x2', 'bowls of instant ramen')],
'objective_function': '9.48x0 + 4.93x1 + 1.09x2',
'constraints': [
'19x0 + 12x1 >= 60',
'17x1 + 13x2 <= 100',
'11x0 + 17x1 <= 132',
'11x0 + 13x2 <= 77',
'11x0 + 17x1 + 13x2 <= 77',
'12x1 + 9x2 <= 181',
'19x0 + 9x2 <= 143',
'19x0 + 12x1 <= 183',
'19x0 + 12x1 + 9x2 <= 183'
]
}
```

## 6: Implement the problem in Gurobi
```python
import gurobi

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

# Define the variables
tomatoes = model.addVar(name="tomatoes", lb=0, ub=gurobi.GRB.INFINITY, integrality=1)
corn_cobs = model.addVar(name="corn cobs", lb=0, ub=gurobi.GRB.INFINITY, integrality=1)
bowls_of_instant_ramen = model.addVar(name="bowls_of_instant_ramen", lb=0, ub=gurobi.GRB.INFINITY)

# Define the objective function
model.setObjective(9.48 * tomatoes + 4.93 * corn_cobs + 1.09 * bowls_of_instant_ramen, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(19 * tomatoes + 12 * corn_cobs >= 60)
model.addConstr(17 * corn_cobs + 13 * bowls_of_instant_ramen <= 100)
model.addConstr(11 * tomatoes + 17 * corn_cobs <= 132)
model.addConstr(11 * tomatoes + 13 * bowls_of_instant_ramen <= 77)
model.addConstr(11 * tomatoes + 17 * corn_cobs + 13 * bowls_of_instant_ramen <= 77)
model.addConstr(12 * corn_cobs + 9 * bowls_of_instant_ramen <= 181)
model.addConstr(19 * tomatoes + 9 * bowls_of_instant_ramen <= 143)
model.addConstr(19 * tomatoes + 12 * corn_cobs <= 183)
model.addConstr(19 * tomatoes + 12 * corn_cobs + 9 * bowls_of_instant_ramen <= 183)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Tomatoes: ", tomatoes.varValue)
    print("Corn Cobs: ", corn_cobs.varValue)
    print("Bowls of Instant Ramen: ", bowls_of_instant_ramen.varValue)
else:
    print("The model is infeasible")
```