To solve the given optimization problem using Gurobi, we first need to understand and possibly simplify or clarify the constraints provided. The objective function is to minimize \(6x_0 + 2x_1\), where \(x_0\) represents the number of fruit salads and \(x_1\) represents the number of cantaloupes.

Given resources/attributes are:
- 'r0': grams of fiber, with an upper bound of 285 grams. Fruit salads contain 3 grams of fiber each, and cantaloupes contain 15 grams of fiber each.
- 'r1': grams of protein, with an upper bound of 252 grams (though a tighter constraint of 216 is mentioned later). Fruit salads contain 11 grams of protein each, and cantaloupes contain 17 grams of protein each.
- 'r2': grams of fat, with an upper bound of 224 grams. However, the maximum allowed fat is later constrained to 211 grams. Fruit salads contain 1 gram of fat each, and cantaloupes contain 21 grams of fat each.

Constraints:
1. At least 57 grams of fiber: \(3x_0 + 15x_1 \geq 57\)
2. Total protein at least 61 grams: \(11x_0 + 17x_1 \geq 61\)
3. Total fat at least 77 grams: \(x_0 + 21x_1 \geq 77\)
4. Constraint on fiber and protein totals are also given as maximums, but the key minimum requirements are listed above.
5. A unique constraint: \(-10x_0 + 2x_1 \geq 0\)

Given these constraints, we aim to minimize \(6x_0 + 2x_1\) without violating any of them.

Here's how you could set up this problem in Gurobi:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fruit_salads")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="cantaloupes")

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

# Constraints
m.addConstr(3*x0 + 15*x1 >= 57, "at_least_57_fiber")
m.addConstr(11*x0 + 17*x1 >= 61, "at_least_61_protein")
m.addConstr(x0 + 21*x1 >= 77, "at_least_77_fat")
m.addConstr(-10*x0 + 2*x1 >= 0, "unique_constraint")
m.addConstr(3*x0 + 15*x1 <= 285, "max_fiber")
m.addConstr(11*x0 + 17*x1 <= 216, "max_protein")
m.addConstr(x0 + 21*x1 <= 211, "max_fat")

# Optimize model
m.optimize()

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