To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the constraints and objective function into algebraic terms using these variables.

Let's define:
- $x_1$ as the number of sour cherry candies.
- $x_2$ as the number of sour peach candies.

The objective is to minimize costs. The cost per sour cherry candy is $0.10, and the cost per sour peach candy is $0.12. Thus, the objective function can be represented as:
\[ \text{Minimize: } 0.10x_1 + 0.12x_2 \]

The constraints are:
1. The special mix must contain at least 50 units of citric acid.
   - Each sour cherry candy has 2 units of citric acid, and each sour peach candy has 1 unit of citric acid.
   - Thus, the constraint can be represented as: \(2x_1 + x_2 \geq 50\).

2. The special mix must contain at least 60 units of sugar.
   - Each sour cherry candy has 3 units of sugar, and each sour peach candy has 4 units of sugar.
   - Thus, the constraint can be represented as: \(3x_1 + 4x_2 \geq 60\).

3. There can be at most 10 sour cherry candies in the mixture.
   - This constraint can be represented as: \(x_1 \leq 10\).

4. Non-negativity constraints, since the number of candies cannot be negative:
   - \(x_1 \geq 0\) and \(x_2 \geq 0\).

Thus, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'number of sour cherry candies'), ('x2', 'number of sour peach candies')],
  'objective_function': '0.10*x1 + 0.12*x2',
  'constraints': ['2*x1 + x2 >= 50', '3*x1 + 4*x2 >= 60', 'x1 <= 10', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="sour_cherry_candies", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="sour_peach_candies", lb=0)

# Set the objective function
m.setObjective(0.10*x1 + 0.12*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2*x1 + x2 >= 50, "citric_acid_constraint")
m.addConstr(3*x1 + 4*x2 >= 60, "sugar_constraint")
m.addConstr(x1 <= 10, "max_sour_cherry_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of sour cherry candies: {x1.x}")
    print(f"Number of sour peach candies: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found")
```