## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'bowls of pasta', 'apples', and 'hot dogs', 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 $2 \times (\text{bowls of pasta}) + 9 \times (\text{apples}) + 5 \times (\text{hot dogs})$, which in symbolic notation is $2x_0 + 9x_1 + 5x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $7x_0 + 6x_1 + 9x_2 \geq 13$ (at least 13 milligrams of iron)
2. $7x_1 + 7x_2 \geq 17$ (at least 17 grams of carbohydrates from apples and hot dogs)
3. $4x_0 + 7x_2 \geq 15$ (at least 15 grams of carbohydrates from bowls of pasta and hot dogs)
4. $7x_1 + 2x_2 \geq 22$ (at least 22 grams of fiber from apples and hot dogs)
5. $11x_0 + 2x_2 \geq 18$ (at least 18 grams of fiber from bowls of pasta and hot dogs)
6. $7x_0 + 6x_1 + 9x_2 \leq 25$ (no more than 25 milligrams of iron from bowls of pasta and hot dogs)
7. $7x_0 + 6x_1 + 9x_2 \leq 25$ (same as above, redundant)
8. $4x_0 + 7x_2 \leq 56$ (no more than 56 grams of carbohydrates from bowls of pasta and hot dogs)
9. $7x_1 + 7x_2 \leq 83$ (at most 83 grams of carbohydrates from apples and hot dogs)
10. $4x_0 + 7x_1 + 7x_2 \leq 93$ (up to 93 grams of carbohydrates from all)
11. $4x_0 + 7x_1 + 7x_2 \leq 93$ (same as above, redundant)
12. $11x_0 + 2x_2 \leq 84$ (at most 84 grams of fiber from bowls of pasta and hot dogs)
13. $11x_0 + 7x_1 \leq 100$ (no more than 100 grams of fiber from bowls of pasta and apples)
14. $11x_0 + 7x_1 + 2x_2 \leq 100$ (no more than 100 grams of fiber from all)
15. $10x_1 + 7x_2 \leq 73$ (no more than 73 dollars on apples and hot dogs)
16. $10x_0 + 10x_1 + 7x_2 \leq 73$ (no more than 73 dollars on all)

## 4: Define the symbolic variables and constraints for the Gurobi model
The symbolic variables are:
- $x_0$ for 'bowls of pasta'
- $x_1$ for 'apples'
- $x_2$ for 'hot dogs'

And the constraints are as listed above.

## 5: Create the Gurobi model
We will now create a Gurobi model in Python.

```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="bowls_of_pasta", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x1 = m.addVar(name="apples", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = m.addVar(name="hot_dogs", lb=0, ub=gurobi.GRB.INFINITY, integrality=gurobi.GRB.INTEGER)

# Define the objective function
m.setObjective(2 * x0 + 9 * x1 + 5 * x2, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(7 * x0 + 6 * x1 + 9 * x2 >= 13)
m.addConstr(7 * x1 + 7 * x2 >= 17)
m.addConstr(4 * x0 + 7 * x2 >= 15)
m.addConstr(7 * x1 + 2 * x2 >= 22)
m.addConstr(11 * x0 + 2 * x2 >= 18)
m.addConstr(7 * x0 + 6 * x1 + 9 * x2 <= 25)
m.addConstr(4 * x0 + 7 * x2 <= 56)
m.addConstr(7 * x1 + 7 * x2 <= 83)
m.addConstr(4 * x0 + 7 * x1 + 7 * x2 <= 93)
m.addConstr(11 * x0 + 2 * x2 <= 84)
m.addConstr(11 * x0 + 7 * x1 <= 100)
m.addConstr(11 * x0 + 7 * x1 + 2 * x2 <= 100)
m.addConstr(10 * x1 + 7 * x2 <= 73)
m.addConstr(10 * x0 + 10 * x1 + 7 * x2 <= 73)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("bowls of pasta: ", x0.varValue)
    print("apples: ", x1.varValue)
    print("hot dogs: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 6: Provide the symbolic representation of the problem
The symbolic representation is as follows:

```json
{
    'sym_variables': [
        ('x0', 'bowls of pasta'), 
        ('x1', 'apples'), 
        ('x2', 'hot dogs')
    ], 
    'objective_function': '2*x0 + 9*x1 + 5*x2', 
    'constraints': [
        '7*x0 + 6*x1 + 9*x2 >= 13',
        '7*x1 + 7*x2 >= 17',
        '4*x0 + 7*x2 >= 15',
        '7*x1 + 2*x2 >= 22',
        '11*x0 + 2*x2 >= 18',
        '7*x0 + 6*x1 + 9*x2 <= 25',
        '4*x0 + 7*x2 <= 56',
        '7*x1 + 7*x2 <= 83',
        '4*x0 + 7*x1 + 7*x2 <= 93',
        '11*x0 + 2*x2 <= 84',
        '11*x0 + 7*x1 <= 100',
        '11*x0 + 7*x1 + 2*x2 <= 100',
        '10*x1 + 7*x2 <= 73',
        '10*x0 + 10*x1 + 7*x2 <= 73'
    ]
}
```