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

The variables in this problem are:
- `x1`: representing the number of eggs
- `x2`: representing the number of oranges

The objective function is to maximize: `1.69*x1 + 5.7*x2`

Given the constraints:
1. The total combined grams of protein from eggs plus oranges must be 25 at minimum.
2. The total combined sourness index from eggs and oranges has to be equal to or greater than 16.
3. 7 times the number of eggs, plus -1 times the number of oranges must be greater than or equal to zero.
4. The total combined grams of protein from eggs and oranges has to be 55 or less.
5. The total combined sourness index from eggs plus oranges must be 68 at maximum.
6. There must be a non-fractional amount of eggs (i.e., `x1` is an integer).
7. An integer number of oranges has to be used (i.e., `x2` is an integer).

Let's express these constraints algebraically:
- `3*x1 + 11*x2 >= 25`
- `14*x1 + 9*x2 >= 16`
- `7*x1 - x2 >= 0`
- `3*x1 + 11*x2 <= 55`
- `14*x1 + 9*x2 <= 68`

The symbolic representation of the problem can be summarized as follows:
```json
{
    'sym_variables': [('x1', 'eggs'), ('x2', 'oranges')],
    'objective_function': '1.69*x1 + 5.7*x2',
    'constraints': [
        '3*x1 + 11*x2 >= 25',
        '14*x1 + 9*x2 >= 16',
        '7*x1 - x2 >= 0',
        '3*x1 + 11*x2 <= 55',
        '14*x1 + 9*x2 <= 68'
    ]
}
```

To solve this problem using Gurobi, we need to express it in a form that the solver can understand. The following Python code does this:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="eggs")
x2 = m.addVar(vtype=GRB.INTEGER, name="oranges")

# Set the objective function
m.setObjective(1.69*x1 + 5.7*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 11*x2 >= 25, "Protein_Min")
m.addConstr(14*x1 + 9*x2 >= 16, "Sourness_Min")
m.addConstr(7*x1 - x2 >= 0, "Eggs_Oranges_Balance")
m.addConstr(3*x1 + 11*x2 <= 55, "Protein_Max")
m.addConstr(14*x1 + 9*x2 <= 68, "Sourness_Max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Eggs: {x1.x}")
    print(f"Oranges: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```