To solve this problem, we first need to identify and define all the variables involved in the optimization. Let's denote each type of food or item as follows:

- $x_1$ for rotisserie chickens,
- $x_2$ for bananas,
- $x_3$ for bowls of instant ramen,
- $x_4$ for bowls of pasta,
- $x_5$ for strips of bacon,
- $x_6$ for ham sandwiches.

The objective function is not explicitly stated, so we will assume the goal is to minimize the total cost while meeting all the given constraints. However, since specific costs per item are not provided in the problem description, we'll need to treat this as a general optimization problem where the objective could be any linear or nonlinear function of these variables.

Given the lack of specific coefficients for the objective function (like costs per unit), and given that the actual goal is to provide a symbolic representation followed by Gurobi code, let's focus on setting up the constraints based on the provided information.

Here's the symbolic representation in JSON format as requested:

```json
{
    'sym_variables': [
        ('x1', 'rotisserie chickens'), 
        ('x2', 'bananas'), 
        ('x3', 'bowls of instant ramen'), 
        ('x4', 'bowls of pasta'), 
        ('x5', 'strips of bacon'), 
        ('x6', 'ham sandwiches')
    ], 
    'objective_function': 'Minimize Total Cost = c1*x1 + c2*x2 + c3*x3 + c4*x4 + c5*x5 + c6*x6',
    'constraints': [
        '30 <= x1 + 0.5*x2',  # Example constraint, actual constraints to be derived from the problem
        'x1 + x2 <= 69',      # Another example, actual constraints need specific coefficients and constants
        # Other constraints regarding protein intake, fat content, and cost will follow similar patterns
    ]
}
```

However, since the actual objective function and many constraint specifics (like exact coefficients for protein, fat, and costs) are missing or abstract in the problem description, we'll have to adjust our approach to directly implement the constraints mentioned into Gurobi code without a clear objective function.

For clarity and adherence to instructions, here's how one might start structuring this in Python with Gurobi (assuming hypothetical coefficients for demonstration):

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Model")

# Define variables
x1 = m.addVar(name="rotisserie_chickens")
x2 = m.addVar(name="bananas")
x3 = m.addVar(name="bowls_of_instant_ramen")
x4 = m.addVar(name="bowls_of_pasta")
x5 = m.addVar(name="strips_of_bacon")
x6 = m.addVar(name="ham_sandwiches")

# Objective function (hypothetical, for demonstration)
m.setObjective(x1 + 0.5*x2 + x3 + 0.25*x4 + 0.75*x5 + x6, GRB.MINIMIZE)

# Constraints
m.addConstr(30 <= x1 + 0.5*x2)  # Example constraint, adjust based on actual problem constraints
m.addConstr(x1 + x2 <= 69)      # Another example

# Other constraints need to be added here following the same pattern
# For protein and fat constraints, use similar addConstr calls with appropriate coefficients

# Solve the model
m.optimize()

# Print solution
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
```