To convert the given problem description into a symbolic representation and then into Gurobi code, we first identify the variables and their corresponding natural language objects. The two main variables in this problem are:

- Fruit salads (let's denote this as `x0`)
- Cantaloupes (denoted as `x1`)

The objective function described is to minimize `6*x0 + 2*x1`.

Given constraints can be translated into the following symbolic representation:
- Fiber constraint: `3*x0 + 15*x1 >= 57` and `3*x0 + 15*x1 <= 285`
- Protein constraint: `11*x0 + 17*x1 >= 61` and `11*x0 + 17*x1 <= 216`
- Fat constraint: `1*x0 + 21*x1 >= 77` and `1*x0 + 21*x1 <= 211`
- Additional linear constraint: `-10*x0 + 2*x1 >= 0`

Here is the symbolic representation of the problem:

```json
{
    'sym_variables': [('x0', 'fruit salads'), ('x1', 'cantaloupes')],
    'objective_function': '6*x0 + 2*x1',
    'constraints': [
        '3*x0 + 15*x1 >= 57',
        '3*x0 + 15*x1 <= 285',
        '11*x0 + 17*x1 >= 61',
        '11*x0 + 17*x1 <= 216',
        '1*x0 + 21*x1 >= 77',
        '1*x0 + 21*x1 <= 211',
        '-10*x0 + 2*x1 >= 0'
    ]
}
```

And here is the Gurobi code that solves this optimization problem:

```python
from gurobipy import *

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

# Define variables - both are allowed to be non-integer
x0 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="fruit_salads")
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="cantaloupes")

# Objective function
m.setObjective(6*x0 + 2*x1, GRB.MINIMIZE)

# Constraints
m.addConstr(3*x0 + 15*x1 >= 57, "fiber_min")
m.addConstr(3*x0 + 15*x1 <= 285, "fiber_max")
m.addConstr(11*x0 + 17*x1 >= 61, "protein_min")
m.addConstr(11*x0 + 17*x1 <= 216, "protein_max")
m.addConstr(1*x0 + 21*x1 >= 77, "fat_min")
m.addConstr(1*x0 + 21*x1 <= 211, "fat_max")
m.addConstr(-10*x0 + 2*x1 >= 0, "additional_constraint")

# Optimize model
m.optimize()

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