## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of pasta', 'hot dogs', 'oranges', and 'corn cobs', which can be represented symbolically as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : 'bowls of pasta'
- $x_1$ : 'hot dogs'
- $x_2$ : 'oranges'
- $x_3$ : 'corn cobs'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $8x_0 + 5x_1 + 9x_2 + 6x_3$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3x_2 + 2x_3 \geq 91$
- $7x_0 + x_1 \geq 34$
- $7x_0 + 2x_3 \geq 90$
- $16x_1 + 7x_2 \geq 70$
- $6x_0 + 7x_2 \geq 40$
- $6x_0 + 16x_1 + 2x_3 \geq 68$
- $x_1 + 3x_2 \leq 253$
- $3x_2 + 2x_3 \leq 230$
- $x_1 + 2x_3 \leq 390$
- $7x_0 + 3x_2 \leq 341$
- $7x_0 + x_1 + 3x_2 \leq 122$
- $7x_0 + x_1 + 3x_2 + 2x_3 \leq 122$
- $6x_0 + 7x_2 \leq 437$
- $6x_0 + 2x_3 \leq 185$
- $6x_0 + 16x_1 \leq 229$
- $6x_0 + 16x_1 + 7x_2 + 2x_3 \leq 229$

## 5: Define the resource constraints
The resource constraints are:
- $7x_0 + x_1 + 3x_2 + 2x_3 \leq 406$ (protein)
- $6x_0 + 16x_1 + 7x_2 + 2x_3 \leq 442$ (calcium)

## 6: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

## 7: Implement the Gurobi code
```python
import gurobi

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

# Define the variables
x0 = model.addVar(name='bowls_of_pasta', lb=0)
x1 = model.addVar(name='hot_dogs', lb=0)
x2 = model.addVar(name='oranges', lb=0)
x3 = model.addVar(name='corn_cobs', lb=0)

# Define the objective function
model.setObjective(8*x0 + 5*x1 + 9*x2 + 6*x3, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(3*x2 + 2*x3 >= 91)
model.addConstr(7*x0 + x1 >= 34)
model.addConstr(7*x0 + 2*x3 >= 90)
model.addConstr(16*x1 + 7*x2 >= 70)
model.addConstr(6*x0 + 7*x2 >= 40)
model.addConstr(6*x0 + 16*x1 + 2*x3 >= 68)
model.addConstr(x1 + 3*x2 <= 253)
model.addConstr(3*x2 + 2*x3 <= 230)
model.addConstr(x1 + 2*x3 <= 390)
model.addConstr(7*x0 + 3*x2 <= 341)
model.addConstr(7*x0 + x1 + 3*x2 <= 122)
model.addConstr(7*x0 + x1 + 3*x2 + 2*x3 <= 122)
model.addConstr(6*x0 + 7*x2 <= 437)
model.addConstr(6*x0 + 2*x3 <= 185)
model.addConstr(6*x0 + 16*x1 <= 229)
model.addConstr(6*x0 + 16*x1 + 7*x2 + 2*x3 <= 229)
model.addConstr(7*x0 + x1 + 3*x2 + 2*x3 <= 406)
model.addConstr(6*x0 + 16*x1 + 7*x2 + 2*x3 <= 442)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('bowls_of_pasta: ', x0.varValue)
    print('hot_dogs: ', x1.varValue)
    print('oranges: ', x2.varValue)
    print('corn_cobs: ', x3.varValue)
else:
    print('No optimal solution found')
```

## 8: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'bowls of pasta'), 
        ('x1', 'hot dogs'), 
        ('x2', 'oranges'), 
        ('x3', 'corn cobs')
    ], 
    'objective_function': '8*x0 + 5*x1 + 9*x2 + 6*x3', 
    'constraints': [
        '3*x2 + 2*x3 >= 91',
        '7*x0 + x1 >= 34',
        '7*x0 + 2*x3 >= 90',
        '16*x1 + 7*x2 >= 70',
        '6*x0 + 7*x2 >= 40',
        '6*x0 + 16*x1 + 2*x3 >= 68',
        'x1 + 3*x2 <= 253',
        '3*x2 + 2*x3 <= 230',
        'x1 + 2*x3 <= 390',
        '7*x0 + 3*x2 <= 341',
        '7*x0 + x1 + 3*x2 <= 122',
        '7*x0 + x1 + 3*x2 + 2*x3 <= 122',
        '6*x0 + 7*x2 <= 437',
        '6*x0 + 2*x3 <= 185',
        '6*x0 + 16*x1 <= 229',
        '6*x0 + 16*x1 + 7*x2 + 2*x3 <= 229',
        '7*x0 + x1 + 3*x2 + 2*x3 <= 406',
        '6*x0 + 16*x1 + 7*x2 + 2*x3 <= 442'
    ]
}
```