To solve Robert's problem of creating a super cocktail at minimum cost with specific alcohol and sugar requirements, we first need to translate the natural language description into a symbolic representation. This involves defining variables for the quantities of white and green cocktails to be mixed, formulating an objective function that represents the total cost, and establishing constraints based on the alcohol and sugar content requirements.

Let's denote:
- \(x_1\) as the amount (in kilograms) of the white cocktail,
- \(x_2\) as the amount (in kilograms) of the green cocktail.

The objective is to minimize the total cost. The white cocktail costs $5.5 per kilogram, and the green cocktail costs $12 per kilogram. Thus, the objective function can be written as:
\[ \text{Minimize:} \quad 5.5x_1 + 12x_2 \]

The constraints are based on the requirements for the super cocktail:
1. The super cocktail must have a minimum of 5 kilograms of alcohol.
   - Since the white cocktail has 7% alcohol and the green cocktail has 2% alcohol, we can express this constraint as \(0.07x_1 + 0.02x_2 \geq 5\).

2. The super cocktail must have a minimum of 25 kilograms of sugar.
   - Given that the white cocktail has 10% sugar and the green cocktail has 25% sugar, we can express this constraint as \(0.10x_1 + 0.25x_2 \geq 25\).

Additionally, since Robert cannot mix a negative amount of any cocktail, we have non-negativity constraints:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

Therefore, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'amount of white cocktail'), ('x2', 'amount of green cocktail')],
    'objective_function': '5.5*x1 + 12*x2',
    'constraints': ['0.07*x1 + 0.02*x2 >= 5', '0.10*x1 + 0.25*x2 >= 25', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we will use the following code:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="white_cocktail", lb=0)
x2 = m.addVar(name="green_cocktail", lb=0)

# Set the objective function
m.setObjective(5.5*x1 + 12*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(0.07*x1 + 0.02*x2 >= 5, name="alcohol_constraint")
m.addConstr(0.10*x1 + 0.25*x2 >= 25, name="sugar_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Amount of white cocktail: {x1.x}")
    print(f"Amount of green cocktail: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found.")
```