To solve Gary's diet problem, we need to convert the natural language description into a symbolic representation and then translate it into Gurobi code.

The symbolic representation of the problem is as follows:
```json
{
  'sym_variables': [('x1', 'servings of noodles'), ('x2', 'servings of cakes')],
  'objective_function': '3*x1 + 5*x2',
  'constraints': [
    '5*x1 + 4*x2 <= 100',  # at most 100 units of proteins
    '12*x1 + 8*x2 <= 30',  # at most 30 units of minerals
    'x1 >= 0',  # non-negativity constraint for noodles
    'x2 >= 0'  # non-negativity constraint for cakes
  ]
}
```
Here, `x1` represents the number of servings of noodles and `x2` represents the number of servings of cakes. The objective function is to minimize the total cost, which is $3 per serving of noodles and $5 per serving of cakes.

The constraints are:

* At most 100 units of proteins: `5*x1 + 4*x2 <= 100`
* At most 30 units of minerals: `12*x1 + 8*x2 <= 30`
* Non-negativity constraint for noodles: `x1 >= 0`
* Non-negativity constraint for cakes: `x2 >= 0`

Now, let's translate this symbolic representation into Gurobi code:
```python
from gurobipy import *

# Create a new model
m = Model("Gary's Diet")

# Define the variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="servings_of_noodles")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="servings_of_cakes")

# Define the objective function
m.setObjective(3*x1 + 5*x2, GRB.MINIMIZE)

# Add the constraints
m.addConstr(5*x1 + 4*x2 <= 100, "protein_constraint")
m.addConstr(12*x1 + 8*x2 <= 30, "mineral_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Servings of noodles:", x1.x)
    print("Servings of cakes:", x2.x)
    print("Total cost:", m.objVal)
else:
    print("No optimal solution found")
```