## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of instant ramen', 'cherry pies', and 'blueberry pies'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2x_1 + 9x_2 + 5x_3$.

## Step 3: Define the constraints in symbolic notation
1. The healthiness rating of bowls of instant ramen is 11: $11x_1$,
2. cherry pies have a healthiness rating of 7: $7x_2$,
3. blueberry pies have a healthiness rating of 9: $9x_3$.
4. The total combined healthiness rating from cherry pies and blueberry pies should be 53 or more: $7x_2 + 9x_3 \geq 53$.
5. The total combined healthiness rating from bowls of instant ramen plus blueberry pies should be no less than 31: $11x_1 + 9x_3 \geq 31$.
6. The total combined healthiness rating from bowls of instant ramen, cherry pies, and blueberry pies must be 31 at minimum: $11x_1 + 7x_2 + 9x_3 \geq 31$.
7. 4 times the number of cherry pies, plus -9 times the number of blueberry pies has to be at minimum zero: $4x_2 - 9x_3 \geq 0$.
8. The total combined healthiness rating from bowls of instant ramen plus cherry pies plus blueberry pies must be at most 120: $11x_1 + 7x_2 + 9x_3 \leq 120$.
9. $x_1$ must be an integer, $x_2$ can be fractional, and $x_3$ must be an integer.

## 4: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'bowls of instant ramen'), ('x2', 'cherry pies'), ('x3', 'blueberry pies')],
'objective_function': '2*x1 + 9*x2 + 5*x3',
'constraints': [
    '7*x2 + 9*x3 >= 53',
    '11*x1 + 9*x3 >= 31',
    '11*x1 + 7*x2 + 9*x3 >= 31',
    '4*x2 - 9*x3 >= 0',
    '11*x1 + 7*x2 + 9*x3 <= 120',
    'x1 >= 0', 'x2 >= 0', 'x3 >= 0'
]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="bowls_of_instant_ramen", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="cherry_pies")
    x3 = model.addVar(name="blueberry_pies", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(2*x1 + 9*x2 + 5*x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(7*x2 + 9*x3 >= 53)
    model.addConstr(11*x1 + 9*x3 >= 31)
    model.addConstr(11*x1 + 7*x2 + 9*x3 >= 31)
    model.addConstr(4*x2 - 9*x3 >= 0)
    model.addConstr(11*x1 + 7*x2 + 9*x3 <= 120)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"bowls of instant ramen: {x1.varValue}")
        print(f"cherry pies: {x2.varValue}")
        print(f"blueberry pies: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```