To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using mathematical notation.

Let's define the variables as follows:
- $x_1$ represents the number of peanutbutter sandwiches,
- $x_2$ represents the number of cheeseburgers,
- $x_3$ represents the number of bagged salads.

The objective function to maximize is given by: 
\[7.59x_1^2 + 1.23x_2^2 + 3.08x_3^2\]

The constraints based on the carbohydrate content are:
1. $21x_1^2 + 22x_2^2 \leq 204$
2. $21x_1^2 + 11x_3^2 \leq 312$
3. $22x_2^2 + 11x_3^2 \leq 298$
4. $21x_1 + 22x_2 + 11x_3 \leq 298$

Additionally, we have constraints on the nature of the variables:
- $x_1$ can be a non-whole number (continuous),
- $x_2$ must be a whole number (integer),
- $x_3$ can be a decimal (continuous).

Now, let's represent this problem symbolically as requested:

```json
{
  'sym_variables': [('x1', 'peanutbutter sandwiches'), ('x2', 'cheeseburgers'), ('x3', 'bagged salads')],
  'objective_function': '7.59*x1**2 + 1.23*x2**2 + 3.08*x3**2',
  'constraints': [
    '21*x1**2 + 22*x2**2 <= 204',
    '21*x1**2 + 11*x3**2 <= 312',
    '22*x2**2 + 11*x3**2 <= 298',
    '21*x1 + 22*x2 + 11*x3 <= 298'
  ]
}
```

To solve this problem using Gurobi, we'll write the Python code to model and solve it. Note that Gurobi requires a license and installation on your system.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="peanutbutter_sandwiches")
x2 = m.addVar(vtype=GRB.INTEGER, name="cheeseburgers")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="bagged_salads")

# Define the objective function
m.setObjective(7.59*x1**2 + 1.23*x2**2 + 3.08*x3**2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(21*x1**2 + 22*x2**2 <= 204, name="carb_limit_1")
m.addConstr(21*x1**2 + 11*x3**2 <= 312, name="carb_limit_2")
m.addConstr(22*x2**2 + 11*x3**2 <= 298, name="carb_limit_3")
m.addConstr(21*x1 + 22*x2 + 11*x3 <= 298, name="total_carb_limit")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"peanutbutter sandwiches: {x1.x}")
    print(f"cheeseburgers: {x2.x}")
    print(f"bagged salads: {x3.x}")
else:
    print("No optimal solution found")
```