To solve the given optimization problem, we need to first identify the variables and constraints from the natural language description. The variables in this problem are 'strawberries' and 'milkshakes', which can be represented symbolically as x0 and x1, respectively.

The objective function is described as minimizing 2.92 times the amount of strawberries times milkshakes plus 2.39 multiplied by the number of milkshakes. This can be algebraically represented as 2.92*x0*x1 + 2.39*x1.

There are several constraints given:
- The total combined healthiness rating from strawberries and milkshakes should be at least 20, which translates to 13*x0 + 4*x1 >= 20.
- The total combined healthiness rating must not exceed 46, so we have 13*x0 + 4*x1 <= 46.
- The cost constraint is that you must spend at least $12 on strawberries and milkshakes, which gives us 11*x0 + 2*x1 >= 12.
- Additionally, the total spent should not exceed $37, leading to another constraint: 11*x0 + 2*x1 <= 37.
- There's also a constraint involving the number of items: -3*x0 + 9*x1 >= 0.

Lastly, 'strawberries' must be a whole number (x0 is an integer), and 'milkshakes' can be fractional (x1 is continuous).

Here's the symbolic representation of the problem:
```json
{
    'sym_variables': [('x0', 'strawberries'), ('x1', 'milkshakes')],
    'objective_function': '2.92*x0*x1 + 2.39*x1',
    'constraints': [
        '13*x0 + 4*x1 >= 20',
        '13*x0 + 4*x1 <= 46',
        '11*x0 + 2*x1 >= 12',
        '11*x0 + 2*x1 <= 37',
        '-3*x0 + 9*x1 >= 0'
    ]
}
```

Now, let's write the Gurobi code for this optimization problem:
```python
from gurobipy import *

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

# Add variables: x0 (strawberries) is integer, x1 (milkshakes) is continuous
x0 = m.addVar(vtype=GRB.INTEGER, name="strawberries")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milkshakes")

# Objective function: minimize 2.92*x0*x1 + 2.39*x1
m.setObjective(2.92*x0*x1 + 2.39*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(13*x0 + 4*x1 >= 20, name="healthiness_min")
m.addConstr(13*x0 + 4*x1 <= 46, name="healthiness_max")
m.addConstr(11*x0 + 2*x1 >= 12, name="cost_min")
m.addConstr(11*x0 + 2*x1 <= 37, name="cost_max")
m.addConstr(-3*x0 + 9*x1 >= 0, name="item_constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Strawberries: {x0.x}")
    print(f"Milkshakes: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```