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 objective function and constraints into algebraic terms.

Let's denote:
- \(x_1\) as the amount invested in real estate,
- \(x_2\) as the amount invested in the pharmaceuticals industry.

The objective function aims to maximize the return on investment, which can be calculated as follows:
\[ \text{Maximize: } 0.05x_1 + 0.10x_2 \]

Given that we have $300,000 available for investment, the total amount invested in both areas should not exceed this limit:
\[ x_1 + x_2 \leq 300,000 \]

Additionally, we are advised to place at least 30% of the investment in real estate:
\[ x_1 \geq 0.30 \times 300,000 \]
\[ x_1 \geq 90,000 \]

And, due to recent issues with the pharmaceutical industry, we decide that at most 35% of the investment should be placed in the pharmaceuticals industry:
\[ x_2 \leq 0.35 \times 300,000 \]
\[ x_2 \leq 105,000 \]

Also, both \(x_1\) and \(x_2\) must be non-negative since they represent amounts of money.

Therefore, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'amount invested in real estate'), ('x2', 'amount invested in pharmaceuticals industry')],
    'objective_function': '0.05*x1 + 0.10*x2',
    'constraints': ['x1 + x2 <= 300000', 'x1 >= 90000', 'x2 <= 105000', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:
```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=0, name="Real_Estate")
x2 = m.addVar(lb=0, name="Pharmaceuticals")

# Set objective function
m.setObjective(0.05*x1 + 0.10*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 300000, "Total_Investment")
m.addConstr(x1 >= 90000, "Min_Real_Estate")
m.addConstr(x2 <= 105000, "Max_Pharma")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Amount invested in Real Estate: ${x1.x:.2f}")
    print(f"Amount invested in Pharmaceuticals: ${x2.x:.2f}")
    print(f"Total Return on Investment: ${(0.05*x1.x + 0.10*x2.x):.2f}")
else:
    print("No optimal solution found")
```