## Step 1: Define the symbolic representation of the problem
Let's denote the number of sour cherry candies as $x_1$ and the number of sour peach candies as $x_2$. The objective is to minimize the cost function $0.10x_1 + 0.12x_2$.

## Step 2: Identify the constraints
The constraints based on the problem description are:
1. Citric acid constraint: $2x_1 + x_2 \geq 50$
2. Sugar constraint: $3x_1 + 4x_2 \geq 60$
3. Sour cherry candies limit: $x_1 \leq 10$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 3: Symbolic representation in the required format
The symbolic variables are:
- $x_1$ for sour cherry candies
- $x_2$ for sour peach candies

The objective function is: $0.10x_1 + 0.12x_2$

The constraints are:
- $2x_1 + x_2 \geq 50$
- $3x_1 + 4x_2 \geq 60$
- $x_1 \leq 10$
- $x_1 \geq 0$
- $x_2 \geq 0$

In the required JSON format:
```json
{
'sym_variables': [('x1', 'sour cherry candies'), ('x2', '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'
]
}
```

## 4: Convert the problem into Gurobi code
Now, let's write the Gurobi code in Python to solve this problem:
```python
import gurobipy as gp

# Create a new model
model = gp.Model("CandyMix")

# Define the variables
x1 = model.addVar(name="sour_cherry_candies", lb=0, ub=10, obj=0.10)
x2 = model.addVar(name="sour_peach_candies", lb=0, obj=0.12)

# Add constraints
model.addConstr(2*x1 + x2 >= 50, name="citric_acid")
model.addConstr(3*x1 + 4*x2 >= 60, name="sugar")

# Set the upper bound for x1
model.addConstr(x1 <= 10, name="sour_cherry_limit")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Sour cherry candies: {x1.varValue}")
    print(f"Sour peach candies: {x2.varValue}")
    print(f"Total cost: {model.objVal}")
else:
    print("No optimal solution found.")
```