To tackle this optimization problem, let's first break down the given information into a symbolic representation as requested.

### Symbolic Representation of the Problem:

- **Variables**: We have two main variables:
  - `x1`: representing 'milkshakes'
  - `x2`: representing 'peanutbutter sandwiches'

- **Objective Function**: The goal is to maximize `1.66*x1 + 8.63*x2`.

- **Constraints**:
  1. `8.76*x1 + 11.41*x2 >= 36` (Total protein from milkshakes and peanut butter sandwiches must be at least 36 grams)
  2. `x1 - 9*x2 >= 0` (One times the number of milkshakes plus -9 times the number of peanut butter sandwiches has to be greater than or equal to zero)
  3. `8.76*x1 + 11.41*x2 <= 58` (Total protein from milkshakes and peanut butter sandwiches must not exceed 58 grams)

Given that there may be fractional numbers of both milkshakes and peanut butter sandwiches, we do not need to enforce integer constraints on these variables.

### Symbolic Representation in JSON Format:

```json
{
  'sym_variables': [('x1', 'milkshakes'), ('x2', 'peanutbutter sandwiches')],
  'objective_function': 'maximize 1.66*x1 + 8.63*x2',
  'constraints': [
    '8.76*x1 + 11.41*x2 >= 36',
    'x1 - 9*x2 >= 0',
    '8.76*x1 + 11.41*x2 <= 58'
  ]
}
```

### Gurobi Code:

Now, let's write the Python code using Gurobi to solve this optimization problem.

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milkshakes")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="peanutbutter_sandwiches")

# Set the objective function
m.setObjective(1.66*x1 + 8.63*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(8.76*x1 + 11.41*x2 >= 36, "Total_protein_minimum")
m.addConstr(x1 - 9*x2 >= 0, "Milkshakes_vs_PeanutButterSandwiches")
m.addConstr(8.76*x1 + 11.41*x2 <= 58, "Total_protein_maximum")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
print(f"Objective: {m.objVal}")
```