To solve the optimization problem described, we need to break down the natural language description into a symbolic representation and then convert this representation into Gurobi code.

The variables in the problem are:
- Bowls of instant ramen
- Cherry pies
- Blueberry pies

These can be represented symbolically as:
- \(x_1\): Bowls of instant ramen
- \(x_2\): Cherry pies
- \(x_3\): Blueberry pies

The objective function to minimize is:
\[2x_1 + 9x_2 + 5x_3\]

Given the healthiness ratings and constraints:
- The healthiness rating of bowls of instant ramen is 11.
- Cherry pies have a healthiness rating of 7.
- Blueberry pies have a healthiness rating of 9 each.
- The total combined healthiness rating from cherry pies and blueberry pies should be 53 or more: \(7x_2 + 9x_3 \geq 53\)
- The total combined healthiness rating from bowls of instant ramen plus blueberry pies should be no less than 31: \(11x_1 + 9x_3 \geq 31\)
- The total combined healthiness rating from bowls of instant ramen, cherry pies, and blueberry pies must be 31 at minimum: \(11x_1 + 7x_2 + 9x_3 \geq 31\)
- 4 times the number of cherry pies, plus -9 times the number of blueberry pies has to be at minimum zero: \(4x_2 - 9x_3 \geq 0\)
- The total combined healthiness rating from bowls of instant ramen plus cherry pies plus blueberry pies must be at most 120: \(11x_1 + 7x_2 + 9x_3 \leq 120\)
- You have to have a whole number amount of bowls of instant ramen: \(x_1 \in \mathbb{Z}\)
- You can use a fractional amount of cherry pies: \(x_2 \in \mathbb{R}\)
- You are not allowed to use a non-integer number of blueberry pies: \(x_3 \in \mathbb{Z}\)

The symbolic representation of the problem is:
```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'
  ]
}
```

To implement this in Gurobi, we use the following Python code:
```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="bowls_of_instant_ramem")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="cherry_pies")
x3 = m.addVar(vtype=GRB.INTEGER, name="blueberry_pies")

# Set the objective function
m.setObjective(2*x1 + 9*x2 + 5*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(7*x2 + 9*x3 >= 53, "healthiness_rating_cherry_blueberry")
m.addConstr(11*x1 + 9*x3 >= 31, "healthiness_rating_ramem_blueberry")
m.addConstr(11*x1 + 7*x2 + 9*x3 >= 31, "total_healthiness_rating_min")
m.addConstr(4*x2 - 9*x3 >= 0, "cherry_pies_constraint")
m.addConstr(11*x1 + 7*x2 + 9*x3 <= 120, "total_healthiness_rating_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Bowls of instant ramen:", x1.x)
    print("Cherry pies:", x2.x)
    print("Blueberry pies:", x3.x)
else:
    print("No optimal solution found")
```