## Symbolic Representation

To solve this problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ : Amount invested in NFTs
- $x_2$ : Amount invested in crypto-currency

The objective is to maximize profit. Each dollar invested in NFTs yields $0.30 profit, and each dollar invested in crypto-currency yields $0.40 profit. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 0.30x_1 + 0.40x_2 \]

The constraints are:
1. Total investment up to $5000: $x_1 + x_2 \leq 5000$
2. Minimum of 25% of all money invested must be in NFTs: $x_1 \geq 0.25(x_1 + x_2)$
3. At least $2300 must be in crypto-currency: $x_2 \geq 2300$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'Amount invested in NFTs'), ('x2', 'Amount invested in crypto-currency')],
    'objective_function': '0.30*x1 + 0.40*x2',
    'constraints': [
        'x1 + x2 <= 5000',
        'x1 >= 0.25*(x1 + x2)',
        'x2 >= 2300',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="NFTs", lb=0)  # Amount invested in NFTs
x2 = model.addVar(name="crypto-currency", lb=0)  # Amount invested in crypto-currency

# Objective function: Maximize profit
model.setObjective(0.30*x1 + 0.40*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 5000, name="Total_Investment")  # Total investment up to $5000
model.addConstr(x1 >= 0.25*(x1 + x2), name="NFTs_Min_Investment")  # Minimum of 25% in NFTs
model.addConstr(x2 >= 2300, name="crypto-currency_Min_Investment")  # At least $2300 in crypto-currency

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Amount invested in NFTs: ${x1.varValue:.2f}")
    print(f"Amount invested in crypto-currency: ${x2.varValue:.2f}")
    print(f"Max Profit: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```