## Step 1: Define the symbolic representation of the variables
The variables are 'fruit salads', 'sashimi', and 'bowls of instant ramen', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $1.17x_0^2 + 6.23x_0x_1 + 3.65x_0x_2 + 3.75x_1^2 + 1.87x_1x_2 + 6.35x_0 + 8.12x_1 + 2.37x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $6.94x_0 + 3.98x_1 + 7.82x_2 \leq 133$ (total fat)
- $5.74x_0 + 6.87x_1 + 1.66x_2 \leq 182$ (total iron)
- $3.98x_1 + 7.82x_2 \geq 27$ (fat from sashimi and bowls of instant ramen)
- $6.94x_0 + 7.82x_2 \geq 44$ (fat from fruit salads and bowls of instant ramen)
- $6.94x_0 + 3.98x_1 + 7.82x_2 \geq 44$ (total fat from all sources)
- $5.74x_0 + 1.66x_2 \geq 20$ (iron from fruit salads and bowls of instant ramen)
- $5.74x_0 + 6.87x_1 + 1.66x_2 \geq 40$ (total iron from all sources)
- $5.74x_0 + 6.87x_1 + 1.66x_2 \geq 40$ (same as above, redundant)
- $-10x_1 + 7x_2 \geq 0$
- $-x_0 + 5x_1 \geq 0$
- $6.94^2x_0^2 + 3.98^2x_1^2 + 7.82^2x_2^2 \leq 65$ (fat squared)
- $5.74^2x_0^2 + 6.87^2x_1^2 + 1.66^2x_2^2 \leq 122$ (iron squared)
- $x_0, x_1 \in \mathbb{Z}$ (integer constraints)
- $x_2 \in \mathbb{R}$ (real constraint)

## 4: Create the symbolic representation dictionary
```json
{
    'sym_variables': [
        ('x0', 'fruit salads'), 
        ('x1', 'sashimi'), 
        ('x2', 'bowls of instant ramen')
    ], 
    'objective_function': '1.17*x0^2 + 6.23*x0*x1 + 3.65*x0*x2 + 3.75*x1^2 + 1.87*x1*x2 + 6.35*x0 + 8.12*x1 + 2.37*x2', 
    'constraints': [
        '6.94*x0 + 3.98*x1 + 7.82*x2 <= 133',
        '5.74*x0 + 6.87*x1 + 1.66*x2 <= 182',
        '3.98*x1 + 7.82*x2 >= 27',
        '6.94*x0 + 7.82*x2 >= 44',
        '6.94*x0 + 3.98*x1 + 7.82*x2 >= 44',
        '5.74*x0 + 1.66*x2 >= 20',
        '5.74*x0 + 6.87*x1 + 1.66*x2 >= 40',
        '-10*x1 + 7*x2 >= 0',
        '-x0 + 5*x1 >= 0',
        '6.94^2*x0^2 + 3.98^2*x1^2 + 7.82^2*x2^2 <= 65',
        '5.74^2*x0^2 + 6.87^2*x1^2 + 1.66^2*x2^2 <= 122'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

# Define the variables
x0 = m.addVar(name="fruit_salads", vtype=gurobi.GRB.INTEGER)
x1 = m.addVar(name="sashimi", vtype=gurobi.GRB.INTEGER)
x2 = m.addVar(name="bowls_of_instant_ramen", vtype=gurobi.GRB.CONTINUOUS)

# Objective function
m.setObjective(1.17*x0**2 + 6.23*x0*x1 + 3.65*x0*x2 + 3.75*x1**2 + 1.87*x1*x2 + 6.35*x0 + 8.12*x1 + 2.37*x2, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(6.94*x0 + 3.98*x1 + 7.82*x2 <= 133)
m.addConstr(5.74*x0 + 6.87*x1 + 1.66*x2 <= 182)
m.addConstr(3.98*x1 + 7.82*x2 >= 27)
m.addConstr(6.94*x0 + 7.82*x2 >= 44)
m.addConstr(6.94*x0 + 3.98*x1 + 7.82*x2 >= 44)
m.addConstr(5.74*x0 + 1.66*x2 >= 20)
m.addConstr(5.74*x0 + 6.87*x1 + 1.66*x2 >= 40)
m.addConstr(-10*x1 + 7*x2 >= 0)
m.addConstr(-x0 + 5*x1 >= 0)
m.addConstr(6.94**2*x0**2 + 3.98**2*x1**2 + 7.82**2*x2**2 <= 65)
m.addConstr(5.74**2*x0**2 + 6.87**2*x1**2 + 1.66**2*x2**2 <= 122)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Fruit Salads: ", x0.varValue)
    print("Sashimi: ", x1.varValue)
    print("Bowls of Instant Ramen: ", x2.varValue)
else:
    print("The model is infeasible")
```